# spectrumAnalyzer

## Description

The `spectrumAnalyzer`

object displays frequency-domain signals and
the frequency spectrum of time-domain signals. The scope shows the spectrum view and the
spectrogram view. The object performs spectral estimation using the filter bank method and
Welch's method of averaged modified periodograms. You can customize the spectrum analyzer
display to show the data and the measurement information that you need. For more details, see
Algorithms. The spectrum analyzer can
display the power spectrum of the signal in three units, `Watts`

,
`dBm`

, and `dBW`

. For more information on
how to convert the power within these three units, see Convert the Power Between Units.

To display the spectra of signals in the spectrum analyzer:

Create the

`spectrumAnalyzer`

object and set its properties.Call the object with arguments, as if it were a function.

## Creation

### Description

`scope = spectrumAnalyzer`

creates a
`spectrumAnalyzer`

object that displays the frequency spectrum of real or
complex signals.

`scope = spectrumAnalyzer(Name=Value)`

specifies nondefault
properties for `scope`

using one or more name-value arguments. For example,
to display both spectrum and spectrogram, set `ViewType`

to
`"spectrum-and-spectrogram"`

.

## Properties

### Frequently Used

`InputDomain`

— Domain of input signal

`"time"`

(default) | `"frequency"`

The domain of the input signal you want to visualize, specified as
`"time"`

or `"frequency"`

. If you want to
visualize time-domain signals, the spectrum analyzer transforms the signal to the
frequency spectrum based on the algorithm you specify in the `Method`

property.

#### Scope Window Use

In the **Estimation** tab on the spectrum analyzer toolstrip,
set **Input Domain** to `Time`

or
`Frequency`

.

**Data Types: **`char`

| `string`

`SpectrumType`

— Type of spectrum to display

`"power"`

(default) | `"power-density"`

| `"rms"`

The type of spectrum to display, specified as one of the following:

`"power"`

— Power spectrum.`"power-density"`

— Power spectral density. The power spectral density is the squared magnitude of the spectrum normalized to a bandwidth of 1 hertz.`"rms"`

— Root mean square. The root-mean-square shows the square root of the mean square. Use this option to view the frequency of voltage or current signals.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to `"time"`

.

#### Scope Window Use

In the **Scope** tab on the spectrum analyzer toolstrip, click
the drop down arrow of **Spectrum** to select
`Power`

, `Power Density`

, or
`RMS`

.

To enable these options, set the **Input Domain** on the
**Estimation** tab to `Time`

.

**Data Types: **`char`

| `string`

`ViewType`

— View to display

`"spectrum"`

(default) | `"spectrogram"`

| `"spectrum-and-spectrogram"`

View to display, specified as one of the following:

Wind

`"spectrum"`

— Display the frequency spectrum of signals.`"spectrogram"`

— Display the spectrogram of signals. Spectrogram shows the frequency content over time. Each line of the spectrogram is one periodogram. Time scrolls from the top to the bottom of the display. The most recent spectrogram update is at the top of the display.`"spectrum-and-spectrogram"`

— Display both spectrum and spectrogram.

To learn more about how the spectrum analyzer computes spectrum and spectrogram, see the Algorithms section.

**Tunable: **Yes

#### Scope Window Use

In the **Scope** tab on the spectrum analyzer toolstrip, select
**Spectrum**, **Spectrogram**, or
both.

**Data Types: **`char`

| `string`

`SampleRate`

— Sample rate of input

`10000`

(default) | positive scalar

The sample rate of the input in Hz, specified as a positive scalar.

**Tunable: **Yes

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. In
the **Bandwidth** section, specify **Sample Rate
(Hz)** to a finite scalar.

The spectrum analyzer shows the sample rate in the status bar at the bottom of the display.

**Data Types: **`double`

`Method`

— Spectrum estimation method

`"filter-bank"`

(default) | `"welch"`

Spectrum estimation method, specified as one of the following:

`"filter-bank"`

–– Use an analysis filter bank to estimate the power spectrum. Compared to Welch's method, this method has a lower noise floor, better frequency resolution, and lower spectral leakage and requires fewer samples per update.`"welch"`

–– Use Welch's method of averaged modified periodograms.

For more details on these methods, see Algorithms.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to `"time"`

.

#### Scope Window Use

In the **Estimation** tab of the spectrum analyzer toolstrip,
set **Method** to `Filter bank`

or
`Welch`

.

To enable this parameter, set **Input Domain** to
`Time`

in the **Estimation** tab.

**Data Types: **`char`

| `string`

`PlotAsTwoSidedSpectrum`

— Option to plot a two-sided spectrum

`true`

(default) | `false`

Option to plot a two-sided spectrum, specified as one of the following:

`true`

— Compute and plot two-sided spectral estimates. When the input signal is complex valued, you must set this property to`true`

.`false`

— Compute and plot one-sided spectral estimates. If you set this property to`false`

, then the input signal must be real valued.When you set this property to

`false`

, the Spectrum Analyzer uses power-folding. The*y*-axis values are twice the amplitude that they would be if you were to set this property to`true`

, except at`0`

and the Nyquist frequency. A one-sided power spectral density (PSD) contains the total power of the signal in the frequency interval from DC to half the Nyquist rate. For more information, see`pwelch`

.

**Tunable: **Yes

#### Scope Window Use

Click the **Spectrum** tab or the
**Spectrogram** tab (if enabled) of the Spectrum Analyzer
toolstrip. In the **Trace Options** section, select
**Two-Sided Spectrum** to compute and plot two-sided spectral
estimates.

**Data Types: **`logical`

`FrequencyScale`

— Scale to display frequency

`"linear"`

(default) | `"log"`

Scale to display frequency, specified as one of the following:

`"linear"`

— Use a linear scale to display frequencies on the*x*-axis.`"log"`

— Use a logarithmic scale to display frequencies on the*x*-axis.

**Tunable: **Yes

#### Dependency

To set this property to `"log"`

, set the
`PlotAsTwoSidedSpectrum`

property to
`false`

.

#### Scope Window Use

Click the **Spectrum** tab or the
**Spectrogram** tab (if enabled) of the spectrum analyzer
toolstrip. In the **Scale** section, set the **Frequency
Scale** to `Linear`

or
`Log`

.

To set the **Frequency Scale** to
`Log`

, clear the **Two-Sided Spectrum**
check box in the **Trace Options** section in the
**Spectrum** or the **Spectrogram** tab (if
enabled). If you select the **Two-Sided Spectrum** check box, then
you must set the **Frequency Scale** to
`Linear`

.

**Data Types: **`char`

| `string`

`PlotType`

— Plot type for normal traces

`"line"`

(default) | `"stem"`

Type of plot to use for displaying normal traces, specified as
`"line"`

or `"stem"`

. Normal traces are traces
that display free-running spectral estimates.

**Tunable: **Yes

#### Dependencies

To enable this property, set:

`ViewType`

to`"spectrum"`

or`"spectrum-and-spectrogram"`

.`PlotNormalTrace`

to`true`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip,
navigate to the **Configuration** section and click
**Settings**. In the spectrum analyzer Settings window that
opens, under **Display and Labels**, set **Plot
Type** to `Line`

or
`Stem`

.

To enable the **Plot Type**, you must:

Select

**Spectrum**in the**Views**section of the**Scope**tab.Enable the

**Normal Trace**check box in the**Trace Options**section of the**Spectrum**tab.

**Data Types: **`char`

| `string`

`AxesScaling`

— Axes scaling mode

`"auto"`

(default) | `"manual"`

| `"onceatstop"`

| `"updates"`

Axes scaling mode, specified as one of the following:

`"auto"`

— The scope scales the axes to fit the data, both during and after simulation.`"manual"`

— The scope does not scale the axes automatically.`"onceatstop"`

— The scope scales the axes when the simulation stops and you call the`release`

function.`"updates"`

— The scope scales the axes after a specific number of visual updates. It determines the number of updates using the`AxesScalingNumUpdates`

property.

**Tunable: **Yes

**Data Types: **`char`

| `string`

`AxesScalingNumUpdates`

— Number of updates before scaling

100 (default) | positive integer

Number of updates before scaling, specified as a positive integer.

**Tunable: **Yes

#### Dependency

To enable this property, set `AxesScaling`

to `"updates"`

.

**Data Types: **`double`

### Advanced

`FrequencyResolutionMethod`

— Frequency resolution method

`"rbw"`

(default) | `"num-frequency-bands"`

| `"window-length"`

Frequency resolution method of the spectrum analyzer, specified as one of these options:

`"rbw"`

–– The`RBWSource`

and`RBW`

properties control the frequency resolution (in Hz) of the analyzer.`"num-frequency-bands"`

–– Applies only when you set`Method`

to`"filter-bank"`

. The`FFTLengthSource`

and`FFTLength`

properties control the frequency resolution.`"window-length"`

–– Applies only when you set`Method`

to`"welch"`

. The`WindowLength`

property controls the frequency resolution.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to
`"time"`

.

#### Scope Window Use

Click the **Estimation** tab on the Spectrum Analyzer toolstrip.
In the **Frequency Resolution** section, set **Resolution
Method** to one of the available options.

**Data Types: **`char`

| `string`

`RBWSource`

— Source of resolution bandwidth value

`"auto"`

(default) | `"property"`

The source of the resolution bandwidth (RBW), specified as
`"auto"`

or `"property"`

.

`"auto"`

— The spectrum analyzer adjusts the spectral estimation resolution to ensure that there are 1024 RBW intervals over the defined frequency span.`"property"`

— Specify the resolution bandwidth directly using the`RBW`

property.

**Tunable: **Yes

#### Dependency

To enable this property, set `FrequencyResolutionMethod`

to
`"rbw"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Resolution** section, set
**RBW (Hz)** to either `Auto`

or a
positive scalar.

**Data Types: **`char`

| `string`

`RBW`

— Resolution bandwidth

`9.76`

(default) | positive scalar

Resolution bandwidth (RBW) in Hz, specified as a positive scalar. Specify the value to ensure that there are at least two RBW intervals over the specified frequency span. The ratio of the overall span to RBW satisfies this condition:

$$\frac{span}{RBW}>2$$

Specify the overall span based on how you set the `FrequencySpan`

property.

RBW controls the spectral resolution of the displayed signal. The RBW value determines the spacing between frequencies that can be resolved. A smaller value gives a higher spectral resolution and lowers the noise floor. That is, the spectrum analyzer can resolve frequencies that are closer to each other. However, this comes at the cost of a longer sweep time.

**Tunable: **Yes

#### Dependency

To enable this property, set `RBWSource`

to `"property"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Resolution** section, set
**RBW (Hz)** to a positive scalar.

The spectrum analyzer shows the value of RBW in the status bar at the bottom of the display.

**Data Types: **`double`

`WindowLength`

— Window length

`1024`

(default) | integer greater than 2

*Since R2024a*

Window length in samples that the object uses to compute the spectral estimates, specified as an integer greater than 2. This property controls the frequency resolution.

**Tunable: **Yes

#### Dependencies

To enable this property, set:

`Method`

to`"welch"`

.`FrequencyResolutionMethod`

to`"window-length"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Resolution** section, set the
**Window Length** to a positive integer greater than 2.

To enable **Window Length**, set:

**Estimation Method**to`Welch`

.**Resolution Method**to`Window length`

.

`FFTLengthSource`

— Source of FFT length

`"auto"`

(default) | `"property"`

Source of the FFT length, specified as one of these:

`"auto"`

–– The value of FFT length depends on the setting of the frequency resolution method. When you set:`FrequencyResolutionMethod`

to`"rbw"`

, the FFT length equals the number of samples per update,*N*. For more details on_{samples}*N*, see the Algorithms section._{samples}`FrequencyResolutionMethod`

to`"window-length"`

, the FFT length equals the value you specify in the`WindowLength`

property or 1024, whichever is larger.`FrequencyResolutionMethod`

to`"num-frequency-bands"`

, the FFT length equals the input frame size (number of rows).

`"property"`

–– The number of FFT points equals the value you specify in the`FFTLength`

property.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`Method`

to`"welch"`

.`Method`

to`"filter-bank"`

and`FrequencyResolutionMethod`

to`"num-frequency-bands"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer toolstrip.
In the **Frequency Resolution** section, set the **FFT
Length** to `Auto`

or a positive
integer.

**Data Types: **`char`

| `string`

`FFTLength`

— Length of FFT

`1024`

(default) | positive integer

*Since R2024a*

Length of the FFT that the spectrum analyzer uses to compute spectral estimates, specified as a positive integer.

**Tunable: **Yes

#### Dependencies

To enable this property, set `FFTLengthSource`

to
`"property"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Resolution** section, set the
**FFT Length** to `Auto`

or a positive
integer.

`FilterSharpness`

— Sharpness of lowpass filter

`0.3`

(default) | nonnegative scalar in the range [0,1]

Sharpness of the prototype lowpass filter, specified as a real nonnegative scalar in the range [0,1].

Increasing the filter sharpness decreases the spectral leakage and gives a more accurate power reading.

**Tunable: **Yes

#### Dependencies

To enable this property, set `Method`

to `"filter-bank"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Resolution** section, move the
**Sharpness** slider.

To enable this parameter, set **Input Domain** to
`Time`

in the **Estimation** tab.

**Data Types: **`double`

`FrequencyVectorSource`

— Source of frequency vector

`"auto"`

(default) | `"property"`

Source of the frequency vector, specified as one of the following:

`"auto"`

— The spectrum analyzer computes the frequency vector based on the frame size of the input signal and the specified sample rate.`"property"`

— Enter a custom vector in the`FrequencyVector`

property.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to `"frequency"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Domain** section, set **Frequency
(Hz)** to either `Auto`

or a monotonically
increasing vector of length equal to the input signal frame size.

To enable the **Frequency (Hz)**, set **Input
Domain** to `Frequency`

.

**Data Types: **`char`

| `string`

`FrequencyVector`

— Custom frequency vector

`[-5000 5000]`

(default) | monotonically increasing vector

Custom frequency vector, specified as a monotonically increasing vector. This
vector determines the *x*-axis of the display. The vector must be
monotonically increasing and must have the same length as the input signal frame
size.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"frequency"`

.`FrequencyVectorSource`

to`"property"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Domain** section, set **Frequency
(Hz)** to either `Auto`

or a monotonically
increasing vector of length equal to the input signal frame size.

To enable the **Frequency (Hz)**, set **Input
Domain** to `Frequency`

.

**Data Types: **`double`

`FrequencySpan`

— Frequency span mode

`"full"`

(default) | `"span-and-center-frequency"`

| `"start-and-stop-frequencies"`

Frequency span mode, specified as one of the following:

`"full"`

–– The spectrum analyzer computes and plots the spectrum over the entire Nyquist Frequency Interval.`"span-and-center-frequency"`

–– The spectrum analyzer computes and plots the spectrum over the interval specified by the`Span`

and`CenterFrequency`

properties.`"start-and-stop-frequencies"`

–– The spectrum analyzer computes and plots the spectrum over the interval specified by the`StartFrequency`

and`StopFrequency`

properties.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to `"time"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Options** section, set
**Frequency Span** to `Full`

,
`Span and Center Frequency`

, or ```
Start and
Stop Frequencies
```

.

To enable the **Frequency Span**, set **Input
Domain** to `Time`

.

**Data Types: **`char`

| `string`

`Span`

— Frequency span to compute spectrum

`10e3`

(default) | real positive scalar

Frequency span over which the spectrum analyzer computes and plots the spectrum,
specified as a positive scalar in Hz. The overall span, defined by this property and
the `CenterFrequency`

property, must fall within the Nyquist Frequency Interval.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`FrequencySpan`

to`"span-and-center-frequency"`

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Options** section, set
**Frequency Span** to ```
Span and Center
Frequency
```

and **Span (Hz)** to a positive
scalar.

To enable the **Frequency Span**, set **Input
Domain** to `Time`

.

**Data Types: **`double`

`CenterFrequency`

— Center of frequency span

`0`

(default) | real scalar

Center of frequency span over which the spectrum analyzer computes and plots the
spectrum, specified as a real scalar in Hz. The overall frequency span, defined by
`Span`

and this property, must fall within the Nyquist Frequency Interval.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`FrequencySpan`

to`"span-and-center-frequency"`

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Options** section, set
**Frequency Span** to ```
Span and Center
Frequency
```

and **Center Frequency (Hz)** to a real
scalar.

To enable the **Frequency Span**, set **Input
Domain** to `Time`

.

**Data Types: **`double`

`StartFrequency`

— Starting frequency in frequency interval

`-5e3`

(default) | real scalar

Starting frequency in the frequency interval over which the spectrum analyzer
computes and plots the spectrum, specified as a real scalar in Hz. The overall span,
which is defined by this property and `StopFrequency`

, must fall within the Nyquist Frequency Interval.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`FrequencySpan`

to`"start-and-stop-frequencies"`

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Options** section, set
**Frequency Span** to ```
Start and Stop
Frequencies
```

and **Start Frequency (Hz)** to a real
scalar.

To enable the **Frequency Span**, set **Input
Domain** to `Time`

.

**Data Types: **`double`

`StopFrequency`

— Ending frequency in frequency interval

`5e3`

(default) | real scalar

Ending frequency in the frequency interval over which the spectrum analyzer
computes and plots the spectrum, specified as a real scalar in Hz. The overall span,
which is defined by this property and the `StartFrequency`

property, must fall within the Nyquist Frequency Interval.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`FrequencySpan`

to`"start-and-stop-frequencies"`

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Frequency Options** section, set
**Frequency Span** to ```
Start and Stop
Frequencies
```

and **Stop Frequency (Hz)** to a real
scalar.

To enable the **Frequency Span**, set **Input
Domain** to `Time`

.

**Data Types: **`double`

`OverlapPercent`

— Percentage of overlap

`0`

(default) | scalar in the range [0 100)

Percentage of overlap between the previous and current buffered data segments, specified as a scalar in the range [0 100). The overlap creates a window segment that the object uses to compute a spectral estimate.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`Method`

to`"welch"`

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Window Options** section, set the
**Overlap (%)**.

To enable the **Overlap (%)**, set **Input
Domain** to `Time`

and
**Method** to `Welch`

in the
**Estimation** tab on the spectrum analyzer toolstrip.

**Data Types: **`double`

`Window`

— Window function

`"hann"`

(default) | `"blackman-harris"`

| `"chebyshev"`

| `"custom"`

| `"flat-top"`

| `"hamming"`

| `"kaiser"`

| `"rectangular"`

Window function, specified as one of the following preset windows. For more information on a window option, click the link to the corresponding function.

Window Option | Corresponding Signal Processing Toolbox™ Function |
---|---|

`"hann"` | `hann` |

`"blackman-harris"` | `blackmanharris` |

`"chebyshev"` | `chebwin` |

`"flat-top"` | `flattopwin` |

`"hamming"` | `hamming` |

`"kaiser"` | `kaiser` |

`"rectangular"` | `rectwin` |

To use your own spectral estimation window, set this property to
`"custom"`

and specify a custom window function in the `CustomWindow`

property.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`Method`

to`"welch"`

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Window Options** section, set the
**Window**.

To enable the **Window**, set **Input Domain**
to `Time`

and **Method** to
`Welch`

in the **Estimation** tab on the
spectrum analyzer toolstrip.

**Data Types: **`char`

| `string`

`CustomWindow`

— Name of custom window function

`"hann"`

(default) | character array | string scalar

Name of custom window function, specified as a character array or string scalar.
The custom window function name must be on the MATLAB^{®} path. Use this property to customize a window function using additional
properties available with the Signal Processing Toolbox.

**Tunable: **Yes

#### Example:

Define and use a custom window function.

function w = my_hann(L) w = hann(L,"periodic") end scope.Window = "custom"; scope.CustomWindow = "my_hann"

#### Dependency

To use this property, set `Window`

to `"custom"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Window Options** section, for the
**Window**, enter a custom window function name.

**Data Types: **`char`

| `string`

`SidelobeAttenuation`

— Sidelobe attenuation of window

`60`

(default) | scalar greater than or equal to `45`

Sidelobe attenuation of the window in decibels (dB), specified as a positive
scalar greater than or equal to `45`

.

**Tunable: **Yes

#### Dependency

To enable this property, set `Window`

to `"chebyshev"`

or
`"kaiser"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Window Options** section, set the
**Attenuation (dB)**.

To enable the **Attenuation (dB)**, set:

**Input Domain**to`Time`

**Method**to`Welch`

**Window**to either`Chebyshev`

or`Kaiser`

in the**Estimation**tab on the spectrum analyzer toolstrip.

**Data Types: **`double`

`AveragingMethod`

— Averaging method

`"vbw"`

(default) | `"exponential"`

Averaging method, specified as one of the following:

`"vbw"`

— Video bandwidth method. The object uses a lowpass filter to smooth the trace and decrease noise. Use the`VBWSource`

and`VBW`

properties to specify the VBW value.`"exponential"`

— Weighted average of samples. The object computes the average over samples weighted by an exponentially decaying forgetting factor. Use the`ForgettingFactor`

property to specify the weighted forgetting factor.

For more information on averaging methods, see Averaging Method.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to `"time"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Averaging** section, set **Averaging
Method** to `VBW`

or
`Exponential`

.

To enable the **Averaging Method**, set **Input
Domain** to `Time`

.

**Data Types: **`char`

| `string`

`ForgettingFactor`

— Forgetting factor of weighted average method

`0.9`

(default) | scalar in the range [0,1]

Forgetting factor of the exponential weighted averaging method, specified as a scalar in the range [0,1].

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

.`AveragingMethod`

to`"exponential"`

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Averaging** section, adjust the slider for
**Forgetting Factor**.

To enable the **Forgetting Factor**, set **Input
Domain** to `Time`

and **Averaging
Method** to `Exponential`

.

**Data Types: **`double`

`VBWSource`

— Source of video bandwidth

`"auto"`

(default) | `"property"`

Source of the video bandwidth (VBW), specified as either `"auto"`

or `"property"`

.

`"auto"`

— The spectrum analyzer adjusts the VBW such that the equivalent forgetting factor is 0.9.`"property"`

— The spectrum analyzer adjusts the VBW using the value specified in the`VBW`

property.

For more details on the video bandwidth method, see Averaging Method.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to `"time"`

and `AveragingMethod`

to `vbw`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Averaging** section, set **VBW
(Hz)** to either `Auto`

or a positive real
scalar less than or equal to **Sample Rate (Hz)**/2.

To enable the **VBW (Hz)**, set **Input
Domain** to `Time`

and **Averaging
Method** to `VBW`

.

**Data Types: **`char`

| `string`

`VBW`

— Video bandwidth

`10`

(default) | positive scalar

Video bandwidth, specified as a positive scalar less than or equal to `SampleRate`

/2. For more details on the video bandwidth method, see Averaging Method.

The spectrum analyzer shows the value of VBW in the status bar at the bottom of the display.

**Tunable: **Yes

#### Dependency

To enable this property, set `VBWSource`

to `"property"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Averaging** section, set **VBW
(Hz)** to either `Auto`

or enter a positive real
scalar that is less than or equal to **Sample Rate (Hz)**/2.

To enable the **VBW (Hz)**, set **Input
Domain** to `Time`

and **Averaging
Method** to `VBW`

.

**Data Types: **`double`

`InputUnits`

— Units of frequency-domain input

`"dBm"`

(default) | `"dBuV"`

* (since R2023b)* | `"dBV"`

| `"dBW"`

| `"Vrms"`

| `"Watts"`

| `"none"`

Units of the frequency-domain input, specified as `"dBm"`

,
`"dBuV"`

* (since R2023b)*,
`"dBV"`

, `"dBW"`

, `"Vrms"`

,
`"Watts"`

, or `"none"`

. The spectrum analyzer
scales frequency data according to the display unit.

**Tunable: **Yes

#### Dependency

To enable this property, set `InputDomain`

to `"frequency"`

.

#### Scope Window Use

Click the **Estimation** tab on the spectrum analyzer
toolstrip. In the **Domain** section, set **Input
Unit**.

To enable the **Input Unit**, set **Input
Domain** to `Frequency`

.

**Data Types: **`char`

| `string`

`SpectrumUnits`

— Units of the spectrum

`"dBm"`

(default) | `"dBFS"`

| `"dBuV"`

* (since R2023b)* | `"dBV"`

| `"dBW"`

| `"Vrms"`

| `"Watts"`

| `"dBm/Hz"`

| `"dBW/Hz"`

| `"dBFS/Hz"`

| `"Watts/Hz"`

| `"auto"`

Units of the spectrum, specified as one of the following:

`"dBm"`

`"dBFS"`

`"dBuV"`

*(since R2023b)*`"dBV"`

`"dBW"`

`"Vrms"`

`"Watts"`

`"dBm/Hz"`

`"dBW/Hz"`

`"dBFS/Hz"`

`"Watts/Hz"`

`"auto"`

**Tunable: **Yes

#### Dependency

The available spectrum units depend on the value you specify in the `SpectrumType`

property.

`InputDomain` | `SpectrumType` | Allowed `SpectrumUnits` |
---|---|---|

`"time"` | `"power"` | `"dBm"` , `"dBW"` ,
`"dBFS"` , `"Watts"` |

`"power-density"` | `"dBm/Hz"` ,
`"dBW/Hz"` ,`"dBFS/Hz"` ,
`"Watts/Hz"` | |

`"rms"` | `"dBuV"` (since R2023b),
`"dBV"` , `"Vrms"` | |

`"frequency"` | ― | `"auto"` , `"dBm"` , `"dBuV"` (since R2023b),
`"dBV"` , `"dBW"` ,
`"Vrms"` , `"Watts"` |

If you set the `InputDomain`

property to
`"frequency"`

and the `SpectrumUnits`

property
to `"auto"`

, the spectrum analyzer assumes the spectrum units to be
equal to input units specified in the `InputUnits`

property. If you set `InputDomain`

to
`"time"`

and `SpectrumUnits`

to any option
other than `"auto"`

, the spectrum analyzer converts the units
specified in `InputUnits`

to the units specified in
`SpectrumUnits`

.

#### Scope Window Use

Click the **Spectrum** tab on the spectrum analyzer toolstrip.
In the **Scale** section, set **Spectrum
Unit**.

**Data Types: **`char`

| `string`

`FullScaleSource`

— Source of full-scale value

`"auto"`

(default) | `"property"`

Source of the dBFS scaling factor, specified as either `"auto"`

or `"property"`

.

`"auto"`

–– The spectrum analyzer adjusts the scaling factor based on the input data.`"property"`

–– Specify the full-scale scaling factor using the`FullScale`

property.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`SpectrumType`

to`"power"`

or`"power-density"`

`SpectrumUnits`

to`"dBFS"`

or`"dBFS/Hz"`

#### Scope Window Use

Click the **Spectrum** tab on the spectrum analyzer toolstrip.
In the **Scale** section, set the **Full Scale**
to either `Auto`

or a positive scalar.

To enable the **Full Scale**:

In the

**Scope**tab, set the spectrum type to`Power`

or`Power Density`

.In the

**Estimation**tab, set**Input Domain**to`Time`

.In the

**Spectrum**tab, set**Spectrum Unit**to`dBFS`

or`dBFS/Hz`

(when spectrum type is set to`Power Density`

).

**Data Types: **`char`

| `string`

`FullScale`

— dBFS full scale

`1`

(default) | positive scalar

dBFS full scale, specified a positive scalar.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

`SpectrumType`

to`"power"`

or`"power-density"`

`SpectrumUnits`

to`"dBFS"`

or`"dBFS/Hz"`

`FullScaleSource`

to`"property"`

#### Scope Window Use

Click the **Spectrum** tab on the spectrum analyzer toolstrip.
In the **Scale** section, set the **Full Scale**
to either `Auto`

or enter a positive scalar.

To enable the **Full Scale**:

In the

**Scope**tab, set the spectrum type to`Power`

or`Power Density`

.In the

**Estimation**tab, set**Input Domain**to`Time`

.In the

**Spectrum**tab, set**Spectrum Unit**to`dBFS`

or`dBFS/Hz`

(when spectrum type is set to`Power Density`

).

**Data Types: **`double`

`ReferenceLoad`

— Reference load to compute power levels

`1`

(default) | positive scalar

Load that the scope uses as a reference to compute power levels, specified as a positive scalar in Ohms.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`SpectrumType`

to`"power"`

or`"power-density"`

.`SpectrumUnits`

to any option other than`"dBFS"`

or`"dBFS/Hz"`

.

#### Scope Window Use

Click the **Spectrum** tab on the spectrum analyzer toolstrip.
In the **Scale** section, set **Reference Load
(Ω)**.

**Data Types: **`double`

`FrequencyOffset`

— Offset to apply to frequency axis

`0`

(default) | scalar | vector

Offset to apply to the frequency axis (*x*-axis) in units of Hz,
specified as one of the following:

Scalar — Apply the same frequency offset to all channels.

Vector — Apply a specific frequency offset for each channel. The vector length must be equal to the number of input channels.

The overall span must fall within the Nyquist Frequency Interval. You can control the overall span in different ways based on how you set the

`FrequencySpan`

property.

**Tunable: **Yes

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. In
the **Bandwidth** section, set **Offset
(Hz)**.

**Data Types: **`double`

### Spectrogram

`SpectrogramChannel`

— Channel for which spectrogram is plotted

`1`

(default) | positive integer

Channel for which the spectrogram is plotted, specified as a positive integer in
the range [1 *N*], where *N* is the number of input
channels.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to `"spectrogram"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Spectrogram** tab on the spectrum analyzer
toolstrip. In the **Channel** section, select a
**Channel**.

**Data Types: **`double`

`TimeResolutionSource`

— Source of the time resolution

`"auto"`

(default) | `"property"`

Source of the time resolution of each spectrogram line, specified as either
`"auto"`

or `"property"`

.

When you set `RBWSource`

and `TimeResolutionSource`

to
`"auto"`

, then RBW is set such that there are 1024 RBW intervals in
one frequency span. The time resolution is set to 1/`RBW`

.

When `RBWSource`

is set to `"auto"`

and
`TimeResolutionSource`

is set to `"property"`

,
then time resolution becomes the main control and RBW is set to 1/`TimeResolution`

Hz.

When `RBWSource`

is set to `"property"`

and
`TimeResolutionSource`

is set to `"auto"`

, then
RBW becomes the main control and the time resolution is set 1/RBW s.

When both `RBWSource`

and
`TimeResolutionSource`

are set to `"property"`

,
then the specified time resolution value must be equal to or larger than the minimum
attainable time resolution which is defined by 1/RBW. Several spectral estimates are
combined into one spectrogram line to obtain the desired time resolution.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

.`ViewType`

to`"spectrogram"`

or`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Spectrogram** tab on the spectrum analyzer
toolstrip. In the **Time Options** section, set the **Time
Resolution (s)** to `Auto`

or enter a positive
scalar.

To enable the **Time Resolution (s)**, select
**Spectrogram** in the **Scope** tab and set
**Input Domain** to `Time`

in the
**Estimation** tab.

**Data Types: **`char`

| `string`

`TimeResolution`

— Time resolution of each spectrogram line

`0.001`

(default) | positive scalar

Time resolution of each spectrogram line in seconds, specified as a positive scalar.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`InputDomain`

to`"time"`

.`ViewType`

to`"spectrogram"`

or`"spectrum-and-spectrogram"`

.`TimeResolutionSource`

to`"property"`

.

#### Scope Window Use

Click the **Spectrogram** tab on the spectrum analyzer
toolstrip. In the **Time Options** section, set the **Time
Resolution (s)** to `Auto`

or enter a positive
scalar.

To enable the **Time Resolution (s)**, select
**Spectrogram** in the **Scope** tab and set
**Input Domain** to `Time`

in the
**Estimation** tab.

**Data Types: **`double`

`TimeSpanSource`

— Source of time span value

`"auto"`

(default) | `"property"`

Source for the time span of the spectrogram, specified as either one of these:

`"auto"`

–– The spectrogram displays 100 spectrogram lines at any given time.`"property"`

–– The spectrogram uses the time duration you specify in seconds in the`TimeSpan`

property.The time span that you specify must be at least two times larger than the duration of the number of samples required for a spectral update.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to `"spectrogram"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Spectrogram** tab on the spectrum analyzer
toolstrip. In the **Time Options** section, set the **Time
Span (s)** to `Auto`

or enter a positive scalar.

**Data Types: **`char`

| `string`

`TimeSpan`

— Time span of spectrogram

`0.1`

(default) | positive scalar

Time span of the spectrogram display in seconds, specified as a positive scalar. You must set the time span to be at least twice as large as the duration of the number of samples required for a spectral update.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`ViewType`

to`"spectrogram"`

or`"spectrum-and-spectrogram"`

.`TimeSpanSource`

to`"property"`

.

#### Scope Window Use

Click the **Spectrogram** tab on the spectrum analyzer
toolstrip. In the **Time Options** section, set the **Time
Span (s)** to `Auto`

or enter a positive scalar.

**Data Types: **`double`

### Measurements

`MeasurementChannel`

— Channel for which to obtain measurements

`1`

(default) | positive integer

The channel for which you need to obtain measurements, specified as a positive
integer in the range [1 *N*], where *N* is the
number of input channels.

**Tunable: **Yes

#### Scope Window Use

Click the **Measurements** tab on the spectrum analyzer
toolstrip. In the **Channel** section, select a
**Channel**.

**Data Types: **`double`

`ChannelMeasurements`

— Channel measurements

`ChannelMeasurementsConfiguration`

object

Channel measurements, specified as a `ChannelMeasurementsConfiguration`

object. Enable channel measurements to
compute and display the occupied bandwidth or adjacent channel power ratio. All
`ChannelMeasurementsConfiguration`

properties are tunable.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to either `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Channel Measurements** tab on the spectrum analyzer
toolstrip and modify the measurement settings.

The **Channel Measurements** tab appears when you select
**Spectrum** in the **Scope** tab.

`CursorMeasurements`

— Cursor measurements

`CursorMeasurementsConfiguration`

object

Cursor measurements, specified as a `CursorMeasurementsConfiguration`

object. Enable cursor measurements to
display waveform cursors. All `CursorMeasurementsConfiguration`

properties are tunable.

**Tunable: **Yes

#### Scope Window Use

Click the **Measurements** tab on the spectrum analyzer
toolstrip and modify the cursor measurements in the **Cursors**
section.

`DistortionMeasurements`

— Distortion measurements

`DistortionMeasurementsConfiguration`

object

Distortion measurements, specified as a `DistortionMeasurementsConfiguration`

object. Enable distortion measurements
to compute and display the harmonic distortion and intermodulation distortion. All
`DistortionMeasurementsConfiguration`

properties are tunable. For
more details, see Distortion Measurements and Harmonic Measurements.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to either `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Measurements** tab on the spectrum analyzer
toolstrip and modify the distortion measurements in the
**Distortion** section.

The **Measurements** tab appears when you select
**Spectrum** in the **Scope** tab.

`PeakFinder`

— Peak finder measurement

`PeakFinderConfiguration`

object

Peak finder measurement, specified as a `PeakFinderConfiguration`

object. Enable peak finder to compute and display
the largest calculated peak values. All `PeakFinderConfiguration`

properties are tunable.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to either `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Measurements** tab on the spectrum analyzer
toolstrip and modify the peak finder measurements in the **Peaks**
section.

The **Measurements** tab appears when you select
**Spectrum** in the **Scope** tab.

`SpectralMask`

— Spectral mask configuration

`SpectralMaskConfiguration`

object

Spectral mask configuration, specified as a `SpectralMaskConfiguration`

object. Use the spectral mask configuration to
draw upper and lower or upper or lower mask lines in the power and power-density
plots. All `SpectralMaskConfiguration`

properties are tunable.

**Tunable: **Yes

#### Dependency

To enable this property, set:

`ViewType`

to either`"spectrum"`

or`"spectrum-and-spectrogram"`

.`SpectrumType`

to either`"power"`

or`"power-density"`

.

#### Scope Window Use

Click the **Spectral Mask** tab on the spectrum analyzer
toolstrip and modify the settings.

The **Spectral Mask** tab appears when you:

Select

**Spectrum**in the**Scope**tab.In the drop-down list under

**Spectrum**, choose either`Power`

or`Power Density`

.

### Visualization

`Name`

— Caption to display in spectrum Analyzer window

`"Spectrum Analyzer"`

(default) | character vector | string scalar

Caption to display in the scope window, specified as a character vector or string scalar.

**Tunable: **Yes

**Data Types: **`char`

| `string`

`Position`

— Spectrum analyzer window position in pixels

`[left bottom 800 500]`

(default) | [`left bottom width height`

]

Spectrum analyzer window position in pixels, specified as a four-element double
vector of the form [`left bottom width height`

]. You can place the
scope window in a specific position on your screen by modifying the values of this
property.

By default, the window appears at the center of your screen with a width of
`800`

pixels and height of `500`

pixels. The exact
center coordinates depend on your screen resolution.

**Tunable: **Yes

`MaximizeAxes`

— Maximize axes control

`"auto"`

(default) | `"on"`

| `"off"`

Maximize axes control, specified as one of the following:

`"auto"`

–– The spectrum analyzer maximizes axes only if the display does not contain any labels or title annotations.`"on"`

–– The spectrum analyzer maximizes axes in all displays.`"off"`

–– The spectrum analyzer does not maximize axes in any display.

**Tunable: **Yes

**Data Types: **`char`

| `string`

`PlotNormalTrace`

— Option to plot normal trace

`true`

(default) | `false`

To remove normal traces from the display, set this property to
`false`

. These traces display the free-running spectral estimates.
The spectrum analyzer continues its spectral computations even when you set this
property to `false`

.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Spectrum** tab on the spectrum analyzer toolstrip
and select the **Normal Trace** check box in the **Trace
Options** section.

To enable the **Normal Trace** check box, select
**Spectrum** in the **Scope** tab.

**Data Types: **`logical`

`PlotMaxHoldTrace`

— Option to plot max-hold trace

`false`

(default) | `true`

Option to plot max-hold trace, specified as `true`

or
`false`

. To compute and plot the maximum-hold spectrum of each
input channel, set this property to `true`

. The spectrum analyzer
computes the maximum-hold spectrum at each frequency bin by keeping the maximum value
of all the power spectrum estimates. When you change the value of this property, the
spectrum analyzer resets its maximum-hold computations.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Spectrum** tab on the spectrum analyzer toolstrip
and select the **Max-Hold Trace** check box in the **Trace
Options** section.

To enable the **Max-Hold Trace** check box, select
**Spectrum** in the **Scope** tab.

**Data Types: **`logical`

`PlotMinHoldTrace`

— Option to plot min-hold trace

`false`

(default) | `true`

Option to plot min-hold trace, specified as `true`

or
`false`

. To compute and plot the minimum-hold spectrum of each
input channel, set this property to `true`

. The spectrum analyzer
computes the minimum-hold spectrum at each frequency bin by keeping the minimum value
of all the power spectrum estimates. When you change the value of this property, the
spectrum analyzer resets its minimum-hold computations.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Spectrum** tab on the spectrum analyzer toolstrip
and select the **Min-Hold Trace** check box in the **Trace
Options** section.

To enable the **Min-Hold Trace** check box, select
**Spectrum** in the **Scope** tab.

**Data Types: **`logical`

`Title`

— Display title

`''`

(default) | character vector | string scalar

Display title, specified as a character vector or a string scalar.

**Tunable: **Yes

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. In
the **Configuration** section, click
**Settings**. In the spectrum analyzer Settings window that
opens up, under **Display and labels**, enter
**Title**.

**Data Types: **`char`

| `string`

`YLabel`

— *y*-axis label

`''`

(default) | character vector | string scalar

*y*-axis label, specified as a character vector or a string
scalar. The spectrum analyzer displays the label to the left of the
*y*-axis.

Regardless of this property, spectrum analyzer always displays power units as one
of the `SpectrumUnits`

values.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. In
the **Configuration** section, click
**Settings**. In the spectrum analyzer Settings window that
opens up, under **Display and labels**, enter
**Y-Label**.

To enable the **Y-Label**, select **Spectrum**
in the **Scope** tab.

**Data Types: **`char`

| `string`

`YLimits`

— *y*-axis limits

`[-80, 20]`

(default) | [`ymin ymax`

]

*y*-axis limits, specified as a two-element numeric vector of the
form [`ymin ymax`

]. The units of the *y*-axis limits
depend on the `SpectrumUnits`

property.

**Example: **`scope.YLimits = [-10,20]`

**Tunable: **Yes

#### Dependencies

To enable this property, set the `ViewType`

property to `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. In
the **Configuration** section, click
**Settings**. In the spectrum analyzer Settings window that
opens up, under **Display and Labels**, enter **Y-Axis
Limits**.

To enable the **Y-Limits**, select
**Spectrum** in the **Scope** tab.

`ColorLimits`

— Color limits of spectrogram

`[-80, 20]`

(default) | `[colorMin colorMax]`

Color limits of the spectrogram, specified as a two-element numeric vector of the
form [`colorMin colorMax`

]. The units of the color limits directly
depend upon the `SpectrumUnits`

property.

**Example: **`scope.ColorLimits = [-10,20]`

**Tunable: **Yes

#### Dependencies

To enable this property, set the `ViewType`

property to `"spectrogram"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. In
the **Configuration** section, click
**Settings**. In the spectrum analyzer Settings window that
opens up, under **Display and Labels**, enter **Color
Limits**.

To enable the **Color Limits**, select
**Spectrogram** in the **Scope** tab.

`Colormap`

— Color look-up table

`"jet"`

(default) | `"bone"`

| `"cool"`

| `"copper"`

| `"gray"`

| `"hot"`

| `"parula"`

| three-column matrix

Color look-up table, specified as a valid colormap name or a three-column matrix with values in the range [0,1] defining RGB triplets.

**Tunable: **Yes

#### Dependencies

To enable this property, set the `ViewType`

property to `"spectrogram"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. In
the **Configuration** section, click
**Settings**. In the spectrum analyzer Settings window that
opens up, under **Display and Labels**, enter **Color
Map**.

To enable the **Color Map**, select
**Spectrogram** in the **Scope** tab.

**Data Types: **`double`

| `char`

| `string`

`ShowGrid`

— Flag to show grid

`true`

(default) | `false`

Flag to show the grid, specified as `true`

or
`false`

. Set this property to `true`

to show grid
lines in the plot.

**Tunable: **Yes

#### Scope Window Use

Click the **Scope** tab on the Spectrum Analyzer toolstrip. In
the **Configuration** section, click
**Settings**. In the Spectrum Analyzer Settings window that
appears, under **Display and Labels**, select **Show
Grid**.

**Data Types: **`logical`

`ShowLegend`

— Show or hide legend

`false`

(default) | `true`

Show or hide the legend, specified as `true`

or
`false`

. To show a legend with the input names, set this property
to `true`

.

Use the legend to control which signals are visible. In the scope legend, click a
signal name to hide the signal in the scope. To show the signal, click the signal name
again. To show only one signal, right-click the signal name. To show all signals,
press **Esc**.

**Tunable: **Yes

#### Dependencies

To enable this property, set the `ViewType`

property to `"spectrum"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. To
see the legend, click **Legend** in the
**Configuration** section.

To enable the **Legend**, select **Spectrum**
in the **Scope** tab.

**Data Types: **`logical`

`ShowColorbar`

— Show or hide color bar

`true`

(default) | `false`

Show or hide color bar, specified as `true`

or
`false`

.

**Tunable: **Yes

#### Dependencies

To enable this property, set the `ViewType`

property to `"spectrogram"`

or
`"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. To
see the color bar, click **Colorbar** in the
**Configuration** section.

To enable the **Colorbar**, select
**Spectrogram** in the **Scope** tab.

**Data Types: **`logical`

`ChannelNames`

— Channel names

empty cell (default) | cell array of character vectors | array of strings

Channel names in the input data, specified as a cell array of character vectors or an array of strings. The names you specify in this property appear in the following locations:

Legend

**Spectrum Analyzer Settings**>**Color and styling**section**Measurements**and**Channel Measurements**tabs

If you do not specify channel names, the
spectrum analyzer names the channels as `Channel 1`

, ```
Channel
2
```

, and so on.

**Tunable: **Yes

#### Dependency

To see the channel names, set `ShowLegend`

to
`true`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip. To
see the legend, click **Legend** in the
**Configuration** section.

**Data Types: **`char`

`AxesLayout`

— Layout of axes

`"vertical"`

(default) | `"horizontal"`

Layout of the axes, specified as one of `"vertical"`

or
`"horizontal"`

. A vertical layout stacks the spectrum above the
spectrogram. A horizontal layout puts the two views side-by-side.

**Tunable: **Yes

#### Dependency

To enable this property, set `ViewType`

to `"spectrum-and-spectrogram"`

.

#### Scope Window Use

Click the **Scope** tab on the spectrum analyzer toolstrip.
Select **Spectrum** and **Spectrogram**. In the
**Configuration** section, select and update
**Layout**.

**Data Types: **`char`

| `string`

## Usage

### Description

`scope(`

displays the frequency
spectrum of the time-domain signal in the spectrum analyzer. If
`signal`

)`signal`

is a frequency-domain signal, the signal is displayed
directly in the spectrum analyzer.

`scope(signal1,signal2,...,signalN)`

displays the frequency
spectrum of multiple signals in the spectrum analyzer. The number of channels in each
signal can be different but the frame size of each signal should be the same.

### Input Arguments

`signal`

— Input signal or signals to visualize

scalar | vector | matrix

Input signal or signals to visualize, specified as a scalar, vector, or a matrix. The number of channels in each signal can be different but the frame size of each signal should be the same.

This scope supports variable-size input signals. That is, the frame size (number of rows) of the input signals can change during simulation, but the number of channels (number of columns) cannot change.

When you set the `InputDomain`

property to `"time"`

, the input signals can
be real or complex. When you set the `InputDomain`

property to
`"frequency"`

, the input signals must be real.

**Example: **`scope(signal1,signal2)`

#### Scope Window Use

To change the appearance of signals in the spectrum analyzer, click the
**Scope** tab and then click **Settings**.
In the spectrum analyzer Settings window, under **Color and
styling**, select a signal and modify its style, width, color, and marker
type.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `fi`

**Complex Number Support: **Yes

## Object Functions

### Specific to spectrumAnalyzer

`generateScript` | Generate MATLAB script to create scope with current settings |

`getMeasurementsData` | Get the current measurement data displayed on the spectrum analyzer |

`getSpectralMaskStatus` | Get test results of current spectral mask |

`getSpectrumData` | Save spectrum data shown in spectrum analyzer |

`isNewDataReady` | Check spectrum analyzer for new data |

`printToFigure` | Print scope window to MATLAB figure |

### Specific to Scopes

**Note**

If you want to restart the simulation from the beginning, call
`reset`

to clear the scope window display. Do not call
`reset`

after calling `release`

.

## Examples

### Spectrum Analyzer for One-Sided Power Spectrum

View a one-sided power spectrum made from the sum of fixed real sine waves with different amplitudes and frequencies.

Fs = 100e6; % Sample rate fSz = 5000; % Frame size sin1 = dsp.SineWave(1e0,5e6,0,SamplesPerFrame=fSz,SampleRate=Fs); sin2 = dsp.SineWave(1e-1,15e6,0,SamplesPerFrame=fSz,SampleRate=Fs); sin3 = dsp.SineWave(1e-2,25e6,0,SamplesPerFrame=fSz,SampleRate=Fs); sin4 = dsp.SineWave(1e-3,35e6,0,SamplesPerFrame=fSz,SampleRate=Fs); sin5 = dsp.SineWave(1e-4,45e6,0,SamplesPerFrame=fSz,SampleRate=Fs); scope = spectrumAnalyzer(SampleRate=Fs,AveragingMethod="exponential",... PlotAsTwoSidedSpectrum=false,... RBWSource="auto",SpectrumUnits="dBW"); for idx = 1:250 y1 = sin1(); y2 = sin2(); y3 = sin3(); y4 = sin4(); y5 = sin5(); scope(y1+y2+y3+y4+y5+0.0001*randn(fSz,1)); end

Call the `release`

function to let property values and input characteristics change. The scope automatically scales the axes.

release(scope)

Run the `clear`

function to close the Spectrum Analyzer window.

`clear('scope');`

### Spectrum Analyzer For Two-Sided Power Spectrum

View a two-sided power spectrum of a noisy sine wave on the Spectrum Analyzer.

sin = dsp.SineWave(Frequency=100,SampleRate=1000,... SamplesPerFrame=1000); scope = spectrumAnalyzer(SampleRate=sin.SampleRate); for ii = 1:250 x = sin() + 0.05*randn(1000,1); scope(x); end

Call the `release`

function to change property values and input characteristics. The scope automatically scales the axes and updates the display one more time if the internal buffer contains any more data.

release(scope);

Run the MATLAB `clear`

function to close the Spectrum Analyzer window.

`clear('scope');`

### Spectrogram of Chirp Signal

Plot the spectrogram for a chirp signal with added random noise.

Fs = 233e3; frameSize = 20e3; chirp = dsp.Chirp(SampleRate=Fs,SamplesPerFrame=frameSize,... InitialFrequency=11e3,TargetFrequency=11e3+55e3); scope = spectrumAnalyzer(SampleRate=Fs,... AveragingMethod="exponential",... ForgettingFactor=0.3,ViewType="spectrogram",... RBWSource="property",RBW=500,... TimeSpanSource="property",TimeSpan=2); scope.PlotAsTwoSidedSpectrum = false; for idx = 1:50 y = chirp()+0.05*randn(frameSize,1); scope(y); end release(scope)

### Display Frequency Input from Spectral Estimation

Use the Spectrum Analyzer to display frequency input from spectral estimates of sinusoids embedded in white Gaussian noise.

**Initialization**

Create two `dsp.SpectrumEstimator`

objects. Set one object to use the Welch-based spectral estimation technique with a Hann window. Set the other object to use the filter bank estimation. Specify a noisy sine wave input signal with four sinusoids at 0.16, 0.2, 0.205, and 0.25 cycles per sample. View the spectral estimate using the `spectrumAnalyzer`

object.

FrameSize = 420; Fs = 1; Frequency = [0.16 0.2 0.205 0.25]; sinegen = dsp.SineWave(SampleRate=Fs,SamplesPerFrame=FrameSize,... Frequency=Frequency,Amplitude=[2e-5 1 0.05 0.5]); NoiseVar = 1e-10; numAvgs = 8; hannEstimator = dsp.SpectrumEstimator(PowerUnits="dBm",... Window="Hann",FrequencyRange="onesided",... SpectralAverages=numAvgs,SampleRate=Fs); filterBankEstimator = dsp.SpectrumEstimator(PowerUnits="dBm",... Method="Filter bank",FrequencyRange="onesided",... SpectralAverages=numAvgs,SampleRate=Fs); spectrumPlotter = spectrumAnalyzer(InputDomain="frequency",... SampleRate=Fs,SpectrumUnits="dBm",... YLimits=[-120,40],PlotAsTwoSidedSpectrum=false,... ChannelNames={'Hann window','Filter bank'},ShowLegend=true);

**Streaming**

Stream the input. Compare the spectral estimates in the spectrum analyzer.

for i = 1:1000 x = sum(sinegen(),2) + sqrt(NoiseVar)*randn(FrameSize,1); Pse_hann = hannEstimator(x); Pfb = filterBankEstimator(x); spectrumPlotter([Pse_hann,Pfb]) end

### Obtain Measurements Data Programmatically for `spectrumAnalyzer`

object

Compute and display the power spectrum of a noisy sinusoidal input signal using the `spectrumAnalyzer`

MATLAB® object. Measure the peaks, cursor placements, adjacent channel power ratio, and distortion values in the spectrum by enabling these properties:

`PeakFinder`

`CursorMeasurements`

`ChannelMeasurements`

`DistortionMeasurements`

**Initialization**

The input sine wave has two frequencies: 1000 Hz and 5000 Hz. Create two `dsp.SineWave`

System objects to generate these two frequencies. Create a `spectrumAnalyzer`

object to compute and display the power spectrum.

Fs = 44100; Sineobject1 = dsp.SineWave(SamplesPerFrame=1024,PhaseOffset=10,... SampleRate=Fs,Frequency=1000); Sineobject2 = dsp.SineWave(SamplesPerFrame=1024,... SampleRate=Fs,Frequency=5000); SA = spectrumAnalyzer(SampleRate=Fs,SpectrumType="power",... PlotAsTwoSidedSpectrum=false,ChannelNames={'Power spectrum of the input'},... YLimits=[-120 40],ShowLegend=true);

**Enable Measurements Data**

To obtain the measurements, set the `Enabled`

property to `true`

.

SA.CursorMeasurements.Enabled = true; SA.ChannelMeasurements.Enabled = true; SA.PeakFinder.Enabled = true; SA.DistortionMeasurements.Enabled = true;

**Use getMeasurementsData**

Stream in the noisy sine wave input signal and estimate the power spectrum of the signal using the `spectrumAnalyzer`

object. Measure the characteristics of the spectrum. Use the `getMeasurementsData`

function to obtain these measurements programmatically. The `isNewDataReady`

function returns `true`

when there is new spectrum data. Store the measured data in the variable `data`

.

data = []; for Iter = 1:1000 Sinewave1 = Sineobject1(); Sinewave2 = Sineobject2(); Input = Sinewave1 + Sinewave2; NoisyInput = Input + 0.001*randn(1024,1); SA(NoisyInput); if SA.isNewDataReady data = [data;getMeasurementsData(SA)]; end end

The panes at the bottom of the scope window display the measurements that you have enabled. The values in these panes match the values in the last time step of the `data`

variable. You can access the individual fields of `data`

to obtain the various measurements programmatically.

**Compare Peak Values**

Use the `PeakFinder`

property to obtain peak values. Verify that the peak values in the last time step of `data`

match the values in the spectrum analyzer plot.

peakvalues = data.PeakFinder(end).Value

`peakvalues = `*3×1*
26.3957
22.7830
-57.9977

frequencieskHz = data.PeakFinder(end).Frequency/1000

`frequencieskHz = `*3×1*
4.9957
0.9905
20.6719

### Print Spectrum Analyzer Scope Display to MATLAB Figure

*Since R2023b*

Use the `printToFigure`

function to print the `spectrumAnalyzer`

object display window to a new MATLAB® figure.

Generate a chirp signal and use the `spectrumAnalyzer`

object to display the spectrum of the chirp.

chirp = dsp.Chirp(SweepDirection="Bidirectional", ... TargetFrequency=2000, ... InitialFrequency=0,... TargetTime=400, ... SweepTime=400, ... SamplesPerFrame=1024, ... SampleRate=4000); scope = spectrumAnalyzer(AveragingMethod="exponential",... ForgettingFactor=0,SampleRate=4000); scope(chirp());

Print the display of the chirp spectrum to a new MATLAB figure. The function returns a handle to the figure.

scopeFig = printToFigure(scope);

The handle to the figure `scopeFig`

lets you modify the appearance and the behavior of the figure window.

Specify a figure name and change the size of the figure to 400-by-250 pixels.

scopeFig.Name="Spectrum of Chirp Signal"; scopeFig.NumberTitle="off"; scopeFig.Position=[1 1 400 250];

When printing to figure, you can make the figure invisible by setting the` Visible`

argument to `false`

.

scopeFig = printToFigure(scope,Visible=false);

## Limitations

Does not support C/C++ code generation using MATLAB Coder™. To generate a standalone application, use the MATLAB Compiler™.

Supports MEX code generation by treating the calls to the object as extrinsic.

## More About

### Convert the Power Between Units

Conversion of power between units on the Spectrum Analyzer window.

The Spectrum Analyzer provides three units to specify the power spectral density:
`Watts/Hz`

, `dBm/Hz`

, and
`dBW/Hz`

. Corresponding units of power are
`Watts`

, `dBm`

, and
`dBW`

. For electrical engineering applications, you can also view
the RMS of your signal in `Vrms`

, `dBuV`

* (since R2023b)*, or `dBV`

. The
default spectrum type is **Power** in `dBm`

.

**Convert the Power in Watts to dBW and dBm**

Power in `dBW`

is given by:

$${P}_{\text{dBW}}=10{\mathrm{log}}_{10}(\text{power}\text{\hspace{0.17em}}\text{in}\text{\hspace{0.17em}}\text{watt}/1\text{\hspace{0.17em}}\text{watt})$$

Power in `dBm`

is given by:

$${P}_{\text{dBm}}=10{\mathrm{log}}_{10}(\text{power}\text{\hspace{0.17em}}\text{in}\text{\hspace{0.17em}}\text{watt}/1\text{\hspace{0.17em}}\text{milliwatt})$$

For a sine wave signal with an amplitude of 1 V, the power of a one-sided spectrum in
`Watts`

is given by:

$$\begin{array}{l}{P}_{\text{Watts}}={A}^{2}/2\\ {P}_{\text{Watts}}=1/2\end{array}$$

Corresponding power in dBm is given by this equation.

$$\begin{array}{l}{P}_{\text{dBm}}=10{\mathrm{log}}_{10}(\text{power}\text{\hspace{0.17em}}\text{in}\text{\hspace{0.17em}}\text{watt}/1\text{\hspace{0.17em}}\text{milliwatt})\\ {P}_{\text{dBm}}=10{\mathrm{log}}_{10}(0.5/{10}^{-3})\\ {P}_{\text{dBm}}=26.9897\text{\hspace{0.17em}}\text{dBm}\end{array}$$

For a white noise signal, the spectrum is flat for all frequencies. Consider a white noise signal with a variance of 1e-4. The power per unit bandwidth therefore (P_{unitbandwidth}) is 1e-4. For a one-sided spectrum in the range [0 *Fs*/2], the total power of white noise in `Watts`

is given by this equation.

$$\begin{array}{l}{P}_{\text{whitenoise}}={P}_{\text{unitbandwidth}}.\text{number}\text{\hspace{0.17em}}\text{of}\text{\hspace{0.17em}}\text{frequency}\text{\hspace{0.17em}}\text{bins},\\ {P}_{\text{whitenoise}}=({10}^{-4}).\left(\frac{Fs/2}{RBW}\right)\end{array}$$

*Fs* is the input sample rate. The number of frequency bins is the ratio of total bandwidth to RBW. For a one-sided spectrum, the total bandwidth is half the sample rate. Consider a sample rate of 44100 Hz and a RBW of 21.53 Hz. With these values, the total power of white noise in `Watts`

is 0.1024 W.

$$\begin{array}{l}{P}_{\text{whitenoise}}=10{\mathrm{log}}_{10}\left(0.1024/{10}^{-3}\right)\\ {P}_{\text{whitenoise}}=20.103\text{\hspace{0.17em}}\text{dBm}\end{array}$$

In dBm, the power of white noise can be calculated using 10*log10(0.1024/10^-3), which equals 20.103 dBm.

**Convert Power in Watts to dBFS**

If you set the spectral units to `dBFS`

and set the full scale (`FullScaleSource`

) to `"auto"`

, power in `dBFS`

is computed as:

$${P}_{\text{dBFS}}=20\cdot {\mathrm{log}}_{10}\left(\sqrt{{P}_{\text{watts}}}/\text{Full\_Scale}\right)$$

where:

`P`

is the power in watts_{watts}For double and float signals,

*Full_Scale*is the maximum value of the input signal.For fixed point or integer signals,

*Full_Scale*is the maximum value that can be represented.

If you specify a manual full scale (set `FullScaleSource`

to `"property"`

), power in `dBFS`

is given by:

$${P}_{\text{FS}}=20\cdot {\mathrm{log}}_{10}\left(\sqrt{{P}_{\text{watts}}}/\text{FS}\right)$$

Where `FS`

is the full-scaling factor specified in the `FullScale`

property.

For a sine wave signal with an amplitude of 1 V, the power of a one-sided spectrum in
`Watts`

is given by:

$$\begin{array}{l}{P}_{\text{Watts}}={A}^{2}/2\\ {P}_{\text{Watts}}=1/2\end{array}$$

The corresponding power in dBFS is given by:

$$\begin{array}{l}{P}_{\text{FS}}=20\cdot {\mathrm{log}}_{10}\left(\sqrt{1/2}/1\right)\\ {P}_{\text{FS}}=-3.0103\text{\hspace{0.17em}}\text{dBFS}\end{array}$$

To confirm the power value in the spectrum analyzer, run these commands:

Fs = 1000; % Sampling frequency sinef = dsp.SineWave(SampleRate=Fs,SamplesPerFrame=100); scope = spectrumAnalyzer(SampleRate=Fs,... SpectrumUnits="dBFS",PlotAsTwoSidedSpectrum=false) for ii = 1:100000 xsine = sinef(); scope(xsine) end

**Peak Finder**in the

**Measurements**tab of the spectrum analyzer toolstrip.

**Convert the Power in dBm to RMS in Vrms**

Power in `dBm`

is given by:

$${P}_{\text{dBm}}=10{\mathrm{log}}_{10}(\text{power}\text{\hspace{0.17em}}\text{in}\text{\hspace{0.17em}}\text{watt}/1\text{\hspace{0.17em}}\text{milliwatt})$$

Voltage in RMS is given by:

$${V}_{\text{rms}}={10}^{{P}_{\text{dBm}}/20}\sqrt{{10}^{-3}}$$

From the previous example, P_{dBm} equals 26.9897 dBm. The V_{rms} is calculated as

$${V}_{\text{rms}}={10}^{26.9897/20}\sqrt{0.001}$$

which equals 0.7071.

To confirm this value using the peak finder:

In the

**Scope**Tab of the Spectrum Analyzer toolstrip, select**Spectrum**>**RMS**.Enable

**Peak Finder**in the**Measurements**tab.

### Cursor Measurements

Measure signal values using vertical waveform cursors that track along the signal.

When you click the **Data Cursors** button in the
**Measurements** tab of the Spectrum Analyzer,
the spectrum display shows vertical cursors on each signal. Each cursor
tracks a vertical line along the signal. The scope displays the difference
between *x*- and *y*-values of the signal
at the two cursors in the box between the cursors.

To enable cursor measurements, click the **Data Cursors** button in the
**Measurements** tab. The cursors appear only
when the Spectrum Analyzer has at least one signal in its display.

You can use the mouse to move the vertical cursors left and right.

In the **Measurements** tab, click the **Data Cursors**
drop-down arrow to select one of these options:

**Snap to Data**— To position the cursors on the signal data points.**Lock Cursor Spacing**— To lock the frequency difference between the two cursors.

For modifying the cursor measurements programmatically, see the `CursorMeasurementsConfiguration`

object.

### Peak Finder Measurements

Compute and display peak values in the scope display.

When you click on the **Peak Finder** button in the **Measurements** tab of the Spectrum Analyzer, an arrow appears on the plot at each maxima and a **Peaks** panel appears at the bottom of the scope window. The Spectrum Analyzer computes peaks from the portion of the input signal that is currently on display in the scope, and the **Peaks** panel shows the peak values and the frequencies at which they occur.

The **Peaks** section in the **Measurements** tab allows you
to specify the number of peaks you want the scope to display, the minimum height above which
you want the scope to detect peaks, the minimum distance between peaks, and label the
peaks.

The Spectrum Analyzer algorithm defines a peak as a local maximum with lower values present on either side of the peak. It does not consider end points as peaks. For more information on the algorithm, see the `findpeaks`

function.

The peaks are valid for any units of the input signal. The letter after the value associated with each measurement indicates the abbreviation for the appropriate International System of Units (SI) prefix, such as *m* for *milli-*. For example, if the input signal is measured in volts, an *m* next to a measurement value indicates that this value is in units of millivolts.

For modifying the peak finder measurements programmatically, see the `PeakFinderConfiguration`

object. For more information on these settings in the
UI, see Peaks.

### Distortion Measurements

Measure harmonic distortion and intermodulation distortion.

When you click the **Distortion** button in the
**Distortion** section of the **Measurements** tab, a
distortion panel opens at the bottom of the Spectrum Analyzer window. This panel shows the
harmonic and distortion measurement values for the input signal currently on display in the
scope. The **Distortion** section in the **Measurements**
tab allows you to specify the distortion type, number of harmonics, and even label the
harmonics.

**Note**

For an accurate measurement, ensure that the fundamental signal (for harmonics) or
primary tones (for intermodulation) is larger than any spurious or harmonic content. To
do so, you may need to adjust the resolution bandwidth (`RBW`

) of the
Spectrum Analyzer. Make sure that the bandwidth is low enough to isolate the signal and
harmonics from spurious noise content. In general, you should set the RBW value such
that there is at least a 10 dB separation between the peaks of the sinusoids and the
noise floor. You also might need to select a different spectral window to obtain a valid
measurement.

You can set the **Distortion Type** parameter to one of these values:

`Harmonic`

–– Select`Harmonic`

if your input is a single sinusoid.`Intermodulation`

–– Select`Intermodulation`

if your input is two equal-amplitude sinusoids. Intermodulation can help you determine distortion when the scope uses only a small portion of the available bandwidth.

See Distortion Measurements for information on how distortion measurements are calculated.

**Harmonic Distortion**

When you set the **Distortion Type** to `Harmonic`

,
these fields appear in the **Harmonic Distortion** panel at the bottom
of the Spectrum Analyzer window.

**H1**— Fundamental frequency in Hz and its power in decibels of the measured power referenced to 1 milliwatt (dBm).**H2, H3, ...**— Harmonics frequencies in Hz and their power in decibels relative to the carrier (dBc). If the harmonics are at the same level or exceed the fundamental frequency, reduce the input power.**THD**— Total harmonic distortion. This value represents the ratio of the power in the harmonics*D*to the power in the fundamental frequency*S*. If the noise power is too high in relation to the harmonics, the THD value is not accurate. In this case, lower the resolution bandwidth or select a different spectral window.$$THD=10\cdot {\mathrm{log}}_{10}(D/S)$$

**SNR**— Signal-to-noise ratio (SNR). This value represents the ratio of the power in the fundamental frequency*S*to the power of all nonharmonic content*N*, including spurious signals, in decibels relative to the carrier (dBc).$$SNR=10\cdot {\mathrm{log}}_{10}(S/N)$$

If you see

`––`

as the reported SNR, the total nonharmonic content of your signal is less than 30% of the total signal.**SINAD**— Signal-to-noise-and-distortion ratio. This value represents the ratio of the power in the fundamental frequency*S*to all other content (including noise*N*and harmonic distortion*D*) in decibels relative to the carrier (dBc).$$SINAD=10\cdot {\mathrm{log}}_{10}\left(\frac{S}{N+D}\right)$$

**SFDR**— Spurious-free dynamic range (SFDR). This value represents the ratio of the power in the fundamental frequency*S*to power of the largest spurious signal*R*regardless of where it falls in the frequency spectrum. The worst spurious signal might or might not be a harmonic of the original signal. SFDR represents the smallest value of a signal that can be distinguished from a large interfering signal. SFDR includes harmonics.$$SNR=10\cdot {\mathrm{log}}_{10}(S/R)$$

The harmonic distortion measurement automatically locates the largest sinusoidal component (fundamental signal frequency). It then computes the harmonic frequencies and power in each harmonic in your signal and ignores any DC component. The measurement does not include any harmonics that are outside the Spectrum Analyzer frequency span. Adjust your frequency span so that it includes all the desired harmonics.

**Note**

To view the best harmonics, make sure that your fundamental frequency is set high enough to resolve the harmonics. However, this frequency should not be so high that aliasing occurs. For the best display of harmonic distortion, your plot should not show skirts, which indicate frequency leakage. The noise floor should be visible.

For a better display, try a Kaiser window with a large sidelobe attenuation (e.g. between 100–300 db).

**Intermodulation Distortion**

When you set the **Distortion Type** to
`Intermodulation`

, the following fields appear in the
**Intermodulation Distortion** panel at the bottom of the Spectrum
Analyzer window.

**F1**— Lower fundamental first-order frequency.**F2**— Upper fundamental first-order frequency.**2F1 - F2**— Lower intermodulation product from third-order harmonics.**2F2 - F1**— Upper intermodulation product from third-order harmonics.**TOI**— Third-order intercept point. If the noise power is too high in relation to the harmonics, the TOI value will not be accurate. In this case, you should lower the resolution bandwidth or select a different spectral window. If the TOI has the same amplitude as the input two-tone signal, reduce the power of that input signal.

The intermodulation distortion measurement automatically locates the fundamental and
the first-order frequencies (*F1* and *F2*). It then
computes the frequencies of the third-order intermodulation products
(2*F1*−*F2* and
2*F2*−*F1*).

For modifying the distortion measurements programmatically, see the `DistortionMeasurementsConfiguration`

object. For more information on these
settings in the UI, see Distortion.

### Channel Measurements

Measure the occupied bandwidth or adjacent channel power ratio (ACPR).

When you click the **Channel Measurements** button in the **Channel
Measurements** tab, a channel measurements panel opens at the bottom of the
Spectrum Analyzer window. This panel displays the occupied bandwidth or the adjacent channel
power ratio measurements. In the **Channel Measurements** tab, you can
specify the occupied bandwidth or the ACPR settings, frequency span, center frequency, and
start and stop frequencies.

You can select the channel measurements **Type** to:

`Occupied BW`

–– Occupied bandwidth`ACPR`

–– Ratio of the power of the main channel to the power of the adjacent channel

For more details on how the Spectrum Analyzer calculates the occupied bandwidth, see Occupied BW.

**Occupied Bandwidth**

When you set the **Type** of channel measurement to compute and display to
`Occupied BW`

, these fields appear in the measurements panel at
the bottom of the scope window.

**Channel Power**— Total power in the channel**Occupied BW**— Bandwidth containing the specified**Occupied BW (%)**of the total power of the spectrum.**Frequency Error**— Difference between the center of the occupied band and the center frequency (**Center Frequency (Hz)**) of the channel

**ACPR**

When you set the **Type** of channel measurement to compute and display to
`ACPR`

, these fields appear in the measurements panel at the
bottom of the scope window.

**Lower (Rel Power (dBc))**— Ratio of the power of the lower sideband to the power of the main channel**Upper (Rel Power (dBc))**— Ratio of the power of the upper sideband to the power of the main channel

To modify the channel measurements programmatically, see the `ChannelMeasurementsConfiguration`

object. For more information on these
settings in the UI, see Channel Measurements.

### Spectral Mask

Visualize spectrum limits and compare spectrum values to specification values.

Add upper and lower masks to the Spectrum Analyzer to visualize spectrum limits and compare
spectrum values to specification values. To enable the **Spectral Mask**
tab, select **Spectrum** in the **Scope** tab. When you
click the **Upper Mask** and **Lower Mask** buttons in
the **Spectral Mask** tab, a **Spectral Mask** panel opens
at the bottom of the Spectrum Analyzer window. This panel provides information on pass-fail
statistics of masks, names of masks currently failing or passing, and names of channels
causing the failure.

You can modify the mask settings in the **Spectral Mask** tab. For more
information on these settings in the UI, see Spectral Mask. For
modifying the channel measurements programmatically, see the `SpectralMaskConfiguration`

object.

**Check Spectral Masks**

You can check the status of the spectral mask using the `getSpectralMaskStatus`

function. This function gives details on the number
of times a mask succeeded or failed, names of channels causing mask failure, and so
on.

You can even use the `MaskTestFailed`

event to perform an action every time
the mask fails. To trigger a function when the mask fails, create a listener to the
`MaskTestFailed`

event and define a callback function to trigger
it. For more details about using events, see Events.

### Customize Visualization

Set configuration and style settings in the Spectrum Analyzer.

To control the settings of the display and labels, color and styling, click on
**Settings** () in the **Scope** tab of the Spectrum
Analyzer toolstrip.

In the dialog box that opens, you can customize the font size, plot type,
*y*-axis properties of the spectrum plot, and color map properties of the
spectrogram plot. You can change the color of the spectrum plot, background, axes, and
labels and also change the line properties.

When you view the spectrum or the spectrogram, you see only the relevant options. For more details about these options, see Configuration > Spectrum Settings.

### Display Controls

Zoom and pan axes using display controls.

To scale the plot axes, use the mouse to pan around the axes and the scroll button on your mouse to zoom in and out of the plot. Additionally, you can use the buttons that appear when you hover over the plot window.

— Maximize the axes, hide all labels and inset the axes values.

— Zoom in on the plot.

— Pan the plot.

— Autoscale the axes to fit the shown data.

## Tips

## Algorithms

### Spectrum Estimation — Filter Bank

When you choose the `Filter Bank`

method, the spectrum
analyzer uses an analysis filter bank to estimate the power spectrum.

The filter bank splits the broadband input signal *x(n)*, of sample
rate *fs*, into multiple narrow band signals
*y _{0}(m)*,

*y*, … ,

_{1}(m)*y*, of sample rate

_{M-1}(m)*fs/M*.

The variable *M* represents the number of frequency bands in the
filter bank. In the spectrum analyzer, *M* is equal to the number of
data points needed to achieve the specified RBW value or 1024, whichever is larger. For
more information on the analysis filter bank and its implementation, see the More About and the Algorithm sections in the
`dsp.Channelizer`

object.

After the spectrum analyzer splits the broadband input signal into multiple narrow
bands, it computes the power in each narrow frequency band using the following equation.
Each *Z _{i}* value is the power estimate over that
narrow frequency band.

$${Z}_{i}=\frac{1}{L}{\displaystyle \sum _{m=0}^{L-1}{\left|{y}_{i}[m]\right|}^{2}}$$

*L* is length of the narrowband signal
*y _{i}(m)* and

*i*= 1, 2, …,

*M*−1.

The power values in all the narrow frequency bands (denoted by
*Z _{i}*) form the

*Z*vector.

$$Z=[{Z}_{0},\text{\hspace{0.17em}}{Z}_{1},\text{\hspace{0.17em}}{Z}_{2},\cdots ,{Z}_{M-1}]$$

The spectrum analyzer averages the current *Z* vector with the
previous *Z* vectors using one of the two moving average methods: video
bandwidth or exponential weighting. The output of the averaging operation forms the
spectral estimate vector. For details on the two averaging methods, see Averaging Method.

The spectrum analyzer uses the value you specify in the **RBW (Hz)**
parameter or the **Number of frequency bands** parameter to determine
the input frame length.

Spectrum analyzer requires a minimum number of samples to compute a spectral estimate.
This value is directly related to the resolution bandwidth,
*RBW*.

When you specify the **Resolution Method** to
`RBW`

, and you set **RBW (Hz)** to:

`Auto`

–– The spectrum analyzer requires 1024 samples to update the display. The spectrum analyzer determines the appropriate resolution bandwidth to ensure that there are 1024 RBW intervals over the specified frequency span. When you set**RBW (Hz)**to`Auto`

, the spectrum analyzer calculates RBW using this equation.$$RB{W}_{auto}=\frac{span}{1024}$$

scalar value –– The spectrum analyzer calculates the number of samples

*N*using this equation._{samples}$${N}_{samples}=\frac{{F}_{s}}{RBW}$$

*F*is the sample rate of the input signal as specified in the_{s}**Sample Rate (Hz)**property.The RBW value you specify must be such that there are at least two RBW intervals over the specified frequency span. The ratio of the overall span to RBW must be greater than two.

$$\frac{span}{RBW}>2$$

*span*is the frequency span over which the spectrum analyzer computes and plots the spectrum. To view the**Span (Hz)**in the scope, click the**Estimation**tab on the spectrum analyzer toolstrip and navigate to the**Frequency Options**section. To enable this property, set**Frequency Span**to`Span and Center Frequency`

.

When you specify the **Resolution Method** to ```
Number of
frequency bands
```

, the resulting RBW is given by:

$$RBW\text{}=\frac{Fs}{FFTLength}$$

When the number of input samples is not sufficient to achieve the specified resolution bandwidth, the spectrum analyzer displays a message similar to this one.

The spectrum analyzer removes this message and displays a spectral estimate once you provide enough input samples.

### Spectrum Estimation — Welch's Method

When you select the `Welch`

method, the power spectrum estimate is
the averaged modified periodograms.

The algorithm in the spectrum analyzer consists of these steps:

The block buffers the input into

*N*-point data segments. Each data segment is split into*P*overlapping data segments, each of length*M*, overlapping by*D*points. The data segments can be represented as:$$\begin{array}{l}{x}_{i}(n)=x(n+iD),\text{\hspace{1em}}\text{\hspace{0.05em}}n=0,1,\mathrm{...},M-1\\ \text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}i=0,1,\mathrm{...},P-1\end{array}$$

If

*D*=*M*/2, the overlap is 50%.If

*D*= 0, the overlap is 0%.

Apply a window to each of the

*P*overlapping data segments in the time domain.If you set the

**Resolution Method**to`Window length`

, you can specify the data window length*N*using the_{window}**Window Length**parameter in the**Estimation**tab.If you set the

**Resolution Method**to`RBW`

, the algorithm determines the data window length using this equation, $${N}_{window}=\frac{NENBW\times Fs}{RBW}$$.Then, it partitions the input signal into a number of windowed data segments.

Most window functions afford more influence to the data at the center of the set than to the data at the edges, which represents a loss of information. To mitigate that loss, the individual data sets are commonly overlapped in time. For each windowed segment, compute the periodogram by computing the discrete Fourier transform. Then compute the squared magnitude of the result and divide the result by

*M*.$${P}_{xx}^{i}(f)=\frac{1}{MU}{\left|{\displaystyle \sum _{n=0}^{M-1}{x}_{i}(n)w(n){e}^{-j2\pi fn}}\right|}^{2},\text{\hspace{1em}}\text{\hspace{1em}}i=0,1,\mathrm{...},P-1$$

where U is the normalization factor for the power in the window function and is given by

$$U=\frac{1}{M}{\displaystyle \sum _{n=0}^{M-1}{w}^{2}(n)}$$

You can specify the window using the

**Window**parameter in the**Estimation**tab of the spectrum analyzer toolstrip.The spectrum analyzer calculates and plots the power spectrum, power spectrum density, and RMS using the modified

*Periodogram*estimator. For more information about the Periodogram method, see`periodogram`

.To determine the power spectrum estimate for Welch's method, the spectrum analyzer averages the result of the periodograms for the last

*P*data segments. The averaging reduces the variance, compared to the original*N*-point data segment. For more details on the averaging, see Averaging Method.$$\mathrm{PSD}\left(f\right)=\frac{1}{P}{\displaystyle \sum _{i=0}^{P-1}{P}_{xx}^{i}(f)}$$

The spectrum analyzer computes the power spectral density using:

$$\mathrm{PSD}\left(f\right)=\frac{1}{P*{F}_{s}}{\displaystyle \sum _{i=0}^{P-1}{P}_{xx}^{i}(f)}$$

The power spectrum is the product of the power spectral density and the resolution bandwidth, as given by this equation.

$${P}_{spectrum}\left(f\right)=\mathrm{PSD}\left(f\right)\times RBW=\mathrm{PSD}\left(f\right)\times \frac{{F}_{s}\times NENBW}{{N}_{window}}$$

The spectrum analyzer plots the power as a spectrogram in the

**Spectrogram**mode. Each line of the spectrogram is one periodogram. The time resolution of each line is 1/*RBW*, which is the minimum attainable resolution. Achieving the resolution you want might require combining several periodograms. You then use interpolation to calculate noninteger values of 1/*RBW*. In the spectrogram display, time scrolls from top to bottom, so the most recent data appears at the top of the display. The offset shows the time value at which the center of the most current spectrogram line occurred.

The spectrum analyzer requires a minimum number of samples to compute a spectral estimate. This value is directly related to the resolution bandwidth (RBW) using this equation, $${N}_{samples}=\frac{\left(1-\frac{{O}_{p}}{100}\right)\times NENBW\times {F}_{s}}{RBW}$$

or to the window length
(*N _{window}*) using this equation,

$${N}_{samples}=\left(1-\frac{{O}_{p}}{100}\right){N}_{window}$$

where O_{p} is the overlap percentage, NENBW is the normalized
effective noise bandwidth, F_{s} is the input sample rate, and RBW is the resolution
bandwidth.

The spectrum analyzer shows the number of samples per update in the spectrum analyzer status bar.

When you specify the **Resolution
Method** to `RBW`

, the window length is given
by,

$${N}_{window}=\frac{NENBW\times {F}_{s}}{RBW}$$

When you specify the **Resolution
Method** to `Window length`

, the algorithm uses
the window length value you specify in the **Window Length** parameter
in the **Estimation** tab on the spectrum analyzer toolstrip.

### Overlap Percentage (*O*_{p})

_{p}

The overlap percentage *O _{p}* is the value you
specify in the

**Overlap %**property. To view the

**Overlap %**in the scope, click the

**Estimation**tab on the spectrum analyzer toolstrip and navigate to the

**Window Options**section.

When you increase the overlap percentage, the spectrum analyzer needs fewer new input samples to compute a new spectral update.

O_{p} | N_{samples} |
---|---|

0% | 100 |

50% | 50 |

80% | 20 |

### Normalized Effective Noise Bandwidth (NENBW)

The normalized effective noise bandwidth *NENBW* is a window
parameter that measures the noise performance of the window. NENBW is determined using
the window length and the window coefficients, and is given by the following
equation:

$$NENBW={N}_{window}\times \frac{{\displaystyle \sum _{n=1}^{{N}_{window}}{w}^{2}(n)}}{{\left[{\displaystyle \sum _{n=1}^{{N}_{window}}w(n)}\right]}^{2}}$$

*w*(*n*) denotes the vector of window coefficients.
*N _{window}* is the window length. For more
information on how the algorithm determines the window length, see the Spectrum
Estimation –– Welch's Method section in Algorithms.

The rectangular window has the smallest NENBW, with a value of 1. All other windows have a larger NENBW value. For example, the Hann window has an NENBW value of approximately 1.5.

The spectrum analyzer shows the value of *NENBW* in the spectrum
analyzer status bar.

You can enable *NENBW* only when you set
**Input Domain** to `Time`

and
**Method** to `Welch`

in the
**Estimation** tab on the spectrum analyzer toolstrip.

### Input Sample Rate (*Fs*)

*F _{s}* is the sample rate of the input signal.
To view the

**Sample Rate (Hz)**in the scope, click the

**Scope**tab on the spectrum analyzer toolstrip and navigate to the

**Bandwidth**section. You can enable this property in the status bar at the bottom of the spectrum analyzer window. Click the icon in the status bar and select

```
Sample
Rate
```

.### Resolution Bandwidth (RBW)

Resolution bandwidth controls the spectral resolution of the displayed signal. The RBW value determines the spacing between frequencies that the scope can resolve. A smaller value gives a higher spectral resolution and lowers the noise floor, that is, the spectrum analyzer can resolve frequencies that are closer to each other. However, this comes at the cost of a longer sweep time.

You can specify the resolution bandwidth using the **RBW (Hz)**
parameter.

If you specify the window length, the scope determines the RBW value from the window length using this equation, $$RBW=\frac{NENBW\times Fs}{{N}_{window}}$$.

When you specify the **Resolution
Method** to `RBW`

, and you set **RBW
(Hz)** to:

`Auto`

–– The spectrum analyzer requires*N*to update the display. The spectrum analyzer determines the appropriate resolution bandwidth to ensure that there are 1024 RBW intervals over the specified frequency span. When you set_{samples}**RBW (Hz)**to`Auto`

, the spectrum analyzer calculates using this equation.$$RB{W}_{auto}=\frac{span}{1024}$$

scalar value –– Specify a value such that there are at least two RBW intervals over the specified frequency span. The ratio of the overall span to RBW must be greater than two:

$$\frac{span}{RBW}>2$$

*span* is the frequency span over which the spectrum analyzer
computes and plots the spectrum. Spectrum analyzer shows the span through the
**Span (Hz)** property. To view the **Span (Hz)**
in the scope, click the **Estimation** tab on the spectrum analyzer
toolstrip, navigate to the **Frequency Options** section, and set
**Frequency Span** to ```
Span and Center
Frequency
```

.

When you specify the **Resolution
Method** to `Number of frequency bands`

, the
resulting RBW is given by:

$$RBW\text{}=\frac{Fs}{FFTLength}$$

When the number of input samples is not sufficient to achieve the specified resolution bandwidth, the spectrum analyzer displays a message similar to this one.

The spectrum analyzer removes this message and displays a spectral estimate once you provide enough input samples.

You can enable this property in the status bar at the bottom of the spectrum analyzer
window. Click the icon in the status bar and select
`RBW`

.

### Nyquist Frequency Interval

When you plot the two-sided spectrum by selecting **Two-Sided Spectrum** in
the **Spectrum**
or **Spectrogram** tab, the Nyquist
frequency interval is $$\left[-\frac{SampleRate}{2},\frac{SampleRate}{2}\right]+FrequencyOffset$$ Hz.

When you clear the **Two-Sided Spectrum**, the Nyquist frequency interval is $$\left[0,\frac{SampleRate}{2}\right]+FrequencyOffset$$ Hz.

### Frequency Vector

When you set **Frequency (Hz)** to `Auto`

, the software calculates the frequency vector for the frequency-domain input.

When you plot the two-sided spectrum by selecting **Two-Sided Spectrum** in
the **Spectrum** or **Spectrogram** tab, the frequency
vector is:

$$\left[-\frac{SampleRate}{2},\frac{SampleRate}{2}\right]$$

When you clear the **Two-Sided Spectrum**, the frequency vector is:

$$\left[0,\frac{SampleRate}{2}\right]$$

### Occupied BW

The spectrum analyzer calculates *Occupied BW* using these steps.

Calculate the total power in the measured frequency range.

Determine the lower frequency value. Starting at the lowest frequency in the range and moving upward, sum the power distributed in each frequency until the result is

$$\frac{100-OccupiedBW\%}{2}$$

of the total power.

Determine the upper frequency value. Starting at the highest frequency in the range and moving downward, sum the power distributed in each frequency until the result reaches

$$\frac{100-OccupiedBW\%}{2}$$

of the total power.

The bandwidth between the lower and upper power frequency values is the occupied bandwidth.

The frequency halfway between the lower and upper frequency values is the center frequency.

### Distortion Measurements

The spectrum analyzer calculates Distortion Measurements using these steps.

Estimate spectral content by finding peaks in the spectrum. When the algorithm detects a peak, it records the width of the peak and clears all monotonically decreasing values by treating all these values as if they belong to the peak. Using this method, the algorithm removes all spectral content centered at DC (0 Hz) from the spectrum and records the amount of bandwidth cleared (

*W*)._{0}Determine the fundamental power (

*P*) from the remaining maximum value of the displayed spectrum. Create a local estimate (_{1}*Fe*) of the fundamental frequency by computing the central moment of the power near the peak. Record the bandwidth of the fundamental power content (_{1}*W*). Then remove the power from the fundamental as in step 1._{1}Determine the power and width of the higher-order harmonics (

*P*,_{2}*W*,_{2}*P*,_{3}*W*, etc.) in succession by examining the frequencies closest to the appropriate multiple of the local estimate (_{3}*Fe*). Remove any spectral content that decreases monotonically about the harmonic frequency from the spectrum before proceeding to the next harmonic._{1}After removing the DC, fundamental, and harmonic content from the spectrum, examine the power of the remaining spectrum for its sum (

*P*), peak value (_{remaining}*P*), and median value (_{maxspur}*P*)._{estnoise}Compute the sum of all the removed bandwidth as

*W*=_{sum}*W*+_{0}*W*+_{1}*W*+...+_{2}*W*._{n}Compute the sum of powers of the second and higher-order harmonics as

*P*=_{harmonic}*P*+_{2}*P*+_{3}*P*+...+_{4}*P*._{n}Estimate the sum of the noise power as:

$${P}_{noise}=({P}_{remaining}\cdot dF+{P}_{est.noise}\cdot {W}_{sum})/RBW$$

Where

*dF*is the absolute difference between frequency bins, and*RBW*is the resolution bandwidth of the window.Then compute the metrics for THD, THD%, SINAD, SNR, and SFDR from the estimates.

$$\begin{array}{l}THD=10\cdot {\mathrm{log}}_{10}\left(\frac{{P}_{harmonic}}{{P}_{1}}\right)\\ THD\%={100.10}^{\left(THD/20\right)}\\ SINAD=10\cdot {\mathrm{log}}_{10}\left(\frac{{P}_{1}}{{P}_{harmonic}+{P}_{noise}}\right)\\ SNR=10\cdot {\mathrm{log}}_{10}\left(\frac{{P}_{1}}{{P}_{noise}}\right)\\ SFDR=10\cdot {\mathrm{log}}_{10}\left(\frac{{P}_{1}}{\mathrm{max}\left({P}_{maxspur},\mathrm{max}\left({P}_{2},{P}_{3},\mathrm{...},{P}_{n}\right)\right)}\right)\end{array}$$

### Harmonic Measurements

The harmonic distortion measurements use the spectrum trace shown in the display as the input to the measurements. The default

`Hann`

window setting of the spectrum analyzer might exhibit leakage that can completely mask the noise floor of the measured signal.The harmonic measurements attempt to correct for leakage by ignoring all frequency content that decreases monotonically away from the maximum of harmonic peaks. If the window leakage covers more than 70% of the frequency bandwidth in your spectrum, you may see a blank reading (–) reported for

**SNR**and**SINAD**. If your application can tolerate the increased equivalent noise bandwidth (ENBW), consider using a Kaiser window with a high attenuation (up to 330 dB) to minimize spectral leakage.Ignore the DC component.

After windowing, the width of each harmonic component masks the noise power in the neighborhood of the fundamental frequency and harmonics. To estimate the noise power in each region, the spectrum analyzer computes the median noise level in the nonharmonic areas of the spectrum. It then extrapolates that value into each region.

*N*^{th}order intermodulation products occur at*A***F1*+*B***F2*,where

*F1*and*F2*are the sinusoid input frequencies and |*A*| + |*B*| =*N*.*A*and*B*are integer values.For intermodulation measurements, compute the third-order intercept (TOI) point as follows.

*TOI*=_{lower}*P*+ (_{F1}*P*-_{F2}*P*)/2_{(2F1-F2)}*TOI*=_{upper}*P*+ (_{F2}*P*-_{F1}*P*)/2_{(2F2-F1)}*TOI*= + (*TOI*+_{lower}*TOI*)/2_{upper}

Where

*P*is power in decibels of the measured power referenced to 1 milliwatt (dBm).

### Averaging Method

The spectrum analyzer can calculate the moving average using two methods:

Video bandwidth — The spectrum analyzer uses a time-domain lowpass filter to smooth the noise in the signal. The video bandwidth (VBW) filter smoothes the trace and decreases noise, and the spectrum analyzer applies the filter to the data before displaying it.

Video bandwidth is the bandwidth of the lowpass filter that spectrum analyzer uses to average or smooth the noise in the signal before displaying it in the scope. The spectrum analyzer computes the video bandwidth using this equation:

$$VBW=\frac{(1-\lambda )RBW}{2\pi \lambda NENBW}$$

where,

Video bandwidth does not affect the level of the noise (noise floor), but only increases the signal-to-noise ratio and smoothes the trace of the noise. When you decrease the value of VBW, the signal-to-noise ratio improves.

The cutoff frequency of the video bandwidth filter is given by:

$${\omega}_{c}=\frac{2\pi VBW}{{F}_{s}/NFFT}$$

where

*Fs*is the input sample rate and NFFT is the number of FFT points.The spectrum analyzer shows the values of sample rate, VBW, and NFFT in the status bar at the bottom of the display. To enable, right-click the status bar and select

`Sample Rate`

,`VBW`

, and`NFFT`

.Exponential — The moving average algorithm uses the exponential weighting method to update the weights and compute the moving average recursively for each

*Z*vector that comes in by using the following recursive equations:$$\begin{array}{l}{w}_{N}=\lambda {w}_{N-1}+1\\ {\overline{z}}_{N}=\left(1-\frac{1}{{w}_{N}}\right){\overline{z}}_{N-1}+\left(\frac{1}{{w}_{N}}\right){z}_{N}\end{array}$$

λ — Forgetting factor

$${w}_{N}$$ — Weighting factor applied to the current

*Z*vector$${z}_{N}$$ — Current

*Z*vector$${\overline{z}}_{N-1}$$ — Moving average until the previous

*Z*vector$$\left(1-\frac{1}{{w}_{N}}\right){\overline{z}}_{N-1}$$ — Effect of the previous

*Z*vectors on the average$${\overline{z}}_{N}$$ — Moving average including the current

*Z*vector

## Version History

**Introduced in R2022a**

### R2024a: **Analyzer** tab has been renamed as **Scope** tab

The **Analyzer** tab in spectrum analyzer has been renamed as
**Scope** tab.

### R2024a: Specify window length and FFT length

You can now specify the window length and FFT length in the spectrum analyzer under certain conditions. These properties are available starting R2024a:

`FrequencyResolutionMethod`

`WindowLength`

`FFTLengthSource`

`FFTLength`

### R2023b: Spectrum Analyzer supports `dBuV`

units

You can set `SpectrumUnits`

to `"dBuV"`

when you
set:

`InputDomain`

to`"time"`

and`SpectrumType`

to`"rms"`

.`InputDomain`

to`"frequency"`

.

You can set `InputUnits`

to `"dBuV"`

when you set
`InputDomain`

to `"frequency"`

.

### R2023b: Support for `printToFigure`

function

You can print the `spectrumAnalyzer`

object display window to a
MATLAB figure using the new `printToFigure`

function.

### R2023a: `SampleRate`

and `PlotAsTwoSidedSpectrum`

properties are tunable

Starting in R2023a, the `SampleRate`

and
`PlotAsTwoSidedSpectrum`

properties are tunable, so you can change the
values of these properties even when the object is `locked`

.

### R2022b: Channel names support array of strings

Starting in R2022b, you can specify the `ChannelNames`

property of
the `spectrumAnalyzer`

object as an array of strings.

sa = spectrumAnalyzer(ChannelNames=["Input", "Output"]);

### R2022b: New `FilterSharpness`

property

When the object uses the filter bank spectral estimation method, you can now increase
the sharpness of the filter bank in the `spectrumAnalyzer`

object. Increasing the filter sharpness decreases the spectral leakage and gives a more
accurate power reading.

### R2022b: New fields on Spectrum Analyzer status bar

Starting in R2022b, the Spectrum Analyzer displays these fields on the status bar:

**Updates**–– This field shows when you select**Spectrum updates processed**in the Customize Status Bar window.Channel number –– This field shows when you select

**Spectrogram channel**in the Customize Status Bar window.The

**Spectrogram channel**field is enabled only when the display shows spectrogram.

To enable these fields, right-click the status bar and select the properties in the Customize Status Bar window.

For more details, see Configure Spectrum Analyzer.

### R2022b: Support for waveform cursors in spectrogram mode

Starting in R2022b, the Spectrum Analyzer supports data cursors even when the scope shows only the spectrogram display.

In the command line, you can edit the `CursorMeasurementsConfiguration`

object when you set the
`ViewType`

to `"spectrogram"`

.

### R2022b: Enhancements to Data Cursor Measurements

Starting in R2022b, the `CursorMeasurementsConfiguration`

object has the new
`LockSpacing`

property. Using this property, you can lock the spacing
between waveform cursors in the scope window.

In the **Measurements** tab of the scope UI window, these data cursor
settings are new:

**Lock cursor spacing**–– This setting corresponds to the`LockSpacing`

property in the`CursorMesaurementsConfiguration`

object.X location –– These fields are enabled and correspond to the

`XLocation`

property in the`CursorMesaurementsConfiguration`

object.

## MATLAB 명령

다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.

명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.

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