Moving average

The `dsp.MovingAverage`

System
object™ computes the moving average of the input signal along each channel,
independently over time. The object uses either the sliding window method or the exponential
weighting method to compute the moving average. In the sliding window method, a window of
specified length is moved over the data, sample by sample, and the average is computed over
the data in the window. In the exponential weighting method, the object multiplies the data
samples with a set of weighting factors. The average is computed by summing the weighted data.
For more details on these methods, see Algorithms.

To compute the moving average of the input:

Create the

`dsp.MovingAverage`

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

To learn more about how System objects work, see What Are System Objects? (MATLAB).

`movAvg = dsp.MovingAverage`

`movAvg = dsp.MovingAverage(Len)`

`movAvg = dsp.MovingAverage(Name,Value)`

returns a moving
average object, `movAvg`

= dsp.MovingAverage`movAvg`

, using the default properties.

sets the `movAvg`

= dsp.MovingAverage(`Len`

)`WindowLength`

property to `Len`

.

specifies additional properties using `movAvg`

= dsp.MovingAverage(`Name,Value`

)`Name,Value`

pairs. Unspecified
properties have default values.

```
movAvg = dsp.MovingAverage('Method','Exponential
weighting','ForgettingFactor',0.9);
```

`y = movAvg(x)`

To use an object function, specify the
System
object as the first input argument. For
example, to release system resources of a System
object named `obj`

, use
this syntax:

release(obj)

[1] Bodenham, Dean. “Adaptive Filtering and Change Detection for Streaming Data.” PH.D. Thesis. Imperial College, London, 2012.

`dsp.MedianFilter`

|`dsp.MovingMaximum`

|`dsp.MovingMinimum`

|`dsp.MovingRMS`

|`dsp.MovingStandardDeviation`

|`dsp.MovingVariance`