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In this tutorial, you will learn how to:

Use the MATLAB Function block to add MATLAB

^{®}functions to Simulink^{®}models for modeling, simulation, and deployment to embedded processors.This capability is useful for coding algorithms that are better stated in the textual language of MATLAB than in the graphical language of Simulink.

Use

`coder.extrinsic`

to call MATLAB code from a MATLAB Function block.This capability allows you to do rapid prototyping. You can call existing MATLAB code from Simulink without having to make this code suitable for code generation.

Check that existing MATLAB code is suitable for code generation before generating code.

You must prepare your code before generating code.

Specify variable-size inputs when generating code.

To complete this tutorial, you should have basic familiarity with MATLAB software. You should also understand how to create and simulate a basic Simulink model.

To complete this tutorial, you must install the following products:

MATLAB

MATLAB Coder™

Simulink

Simulink Coder

C compiler

For a list of supported compilers, see Supported Compilers.

You must set up the C compiler before generating C code. See Setting Up Your C Compiler.

For instructions on installing MathWorks^{®} products,
see the MATLAB installation documentation for your platform.
If you have installed MATLAB and want to check which other MathWorks products
are installed, enter `ver`

in the MATLAB Command
Window.

This section describes the example used by the tutorial. You do not have to be familiar with the algorithm to complete the tutorial.

The example for this tutorial uses a Kalman filter to estimate
the position of an object moving in a two-dimensional space from a
series of noisy inputs based on past positions. The position vector
has two components, `x`

and `y`

,
indicating its horizontal and vertical coordinates.

Kalman filters have a wide range of applications, including control, signal and image processing; radar and sonar; and financial modeling. They are recursive filters that estimate the state of a linear dynamic system from a series of incomplete or noisy measurements. The Kalman filter algorithm relies on the state-space representation of filters and uses a set of variables stored in the state vector to characterize completely the behavior of the system. It updates the state vector linearly and recursively using a state transition matrix and a process noise estimate.

This section describes the algorithm of the Kalman filter and is implemented in the MATLAB version of the filter supplied with this tutorial.

The algorithm predicts the position of a moving object based
on its past positions using a Kalman filter estimator. It estimates
the present position by updating the Kalman state vector, which includes
the position (*x* and *y*), velocity
(*Vx* and *Vy*), and acceleration
(*Ax* and *Ay*) of the moving object.
The Kalman state vector, `x_est`

, is a persistent
variable.

% Initial conditions persistent x_est p_est if isempty(x_est) x_est = zeros(6, 1); p_est = zeros(6, 6); end

`x_est`

is initialized to an
empty `6x1`

column vector and updated each time the
filter is used.The Kalman filter uses the laws of motion to estimate the new state:

$$\begin{array}{l}X={X}_{0}+Vx.dt\\ Y={Y}_{0}+Vy.dt\\ Vx=V{x}_{0}+Ax.dt\\ Vy=V{y}_{0}+Ay.dt\end{array}$$

These laws of motion are captured in the state
transition matrix `A`

, which is a matrix that contains
the coefficient values of `x`

, `y`

, `V`

, _{x}`V`

, _{y}`A`

,
and _{x}`A`

._{y}

% Initialize state transition matrix dt=1; A=[ 1 0 dt 0 0 0;... 0 1 0 dt 0 0;... 0 0 1 0 dt 0;... 0 0 0 1 0 dt;... 0 0 0 0 1 0 ;... 0 0 0 0 0 1 ];

The filtering process has two phases:

Predicted state and covariance

The Kalman filter uses the previously estimated state,

`x_est`

, to predict the current state,`x_prd`

. The predicted state and covariance are calculated in:% Predicted state and covariance x_prd = A * x_est; p_prd = A * p_est * A' + Q;

Estimation

The filter also uses the current measurement,

`z`

, and the predicted state,`x_prd`

, to estimate a more accurate approximation of the current state. The estimated state and covariance are calculated in:% Measurement matrix H = [ 1 0 0 0 0 0; 0 1 0 0 0 0 ]; Q = eye(6); R = 1000 * eye(2);

% Estimation S = H * p_prd' * H' + R; B = H * p_prd'; klm_gain = (S \ B)'; % Estimated state and covariance x_est = x_prd + klm_gain * (z - H * x_prd); p_est = p_prd - klm_gain * H * p_prd; % Compute the estimated measurements y = H * x_est;

Haykin, Simon. *Adaptive Filter Theory*.
Upper Saddle River, NJ: Prentice-Hall, Inc., 1996.

The tutorial uses the following files:

Simulink model files for each step of the tutorial.

Example MATLAB code files for each step of the tutorial.

Throughout this tutorial, you work with Simulink models that call MATLAB files containing a Kalman filter algorithm.

A MAT-file that contains example input data.

A MATLAB file for plotting.

The tutorial files are available in the following folder: `docroot\toolbox\simulink\examples\kalman`

.
To run the tutorial, you must copy these files to a local folder.
For instructions, see Copying Files Locally.

Type | Name | Description |
---|---|---|

MATLAB function files | `ex_kalman01` | Baseline MATLAB implementation of a scalar Kalman filter. |

`ex_kalman02` | Version of the original algorithm suitable for code generation. | |

`ex_kalman03` | Version of Kalman filter suitable for code generation and for use with frame-based and packet-based inputs. | |

`ex_kalman04` | Disabled inlining for code generation. | |

Simulink model files | `ex_kalman00` | Simulink model without a MATLAB Function block. |

`ex_kalman11` | Complete Simulink model with a MATLAB Function block for scalar Kalman filter. | |

`ex_kalman22` | Simulink model with a MATLAB Function block for a Kalman filter that accepts fixed-size (frame-based) inputs. | |

`ex_kalman33` | Simulink model with a MATLAB Function block for a Kalman filter that accepts variable-size (packet-based) inputs. | |

`ex_kalman44` | Simulink model to call `ex_kalman04.m` ,
which has inlining disabled. | |

MATLAB data file | `position` | Contains the input data used by the algorithm. |

Plot files | `plot_trajectory` | Plots the trajectory of the object and the Kalman filter estimated position. |

Copy the tutorial files to a local working folder:

Create a local

folder, for example,`solutions`

`c:\simulink\kalman\solutions`

.Change to the

`docroot\toolbox\simulink\examples`

folder. At the MATLAB command line, enter:cd(fullfile(docroot, 'toolbox', 'simulink', 'examples'))

Copy the contents of the

`kalman`

subfolder to your localfolder, specifying the full path name of the`solutions`

folder:`solutions`

copyfile('kalman', '

*solutions*')For example:

copyfile('kalman', 'c:\simulink\kalman\solutions')

Your

folder now contains a complete set of solutions for the tutorial. If you do not want to perform the steps for each task in the tutorial, you can view the solutions to see how the code should look.`solutions`

Create a local

folder, for example,`work`

`c:\simulink\kalman\work`

.Copy the following files from your

folder to your`solutions`

folder.`work`

`ex_kalman01`

`ex_kalman00`

`position`

`plot_trajectory`

Your

folder now contains all the files that you need to get started with the tutorial.`work`

Building your MATLAB Function block requires
a supported compiler. MATLAB automatically selects one as the
default compiler. If you have multiple MATLAB-supported compilers
installed on your system, you can change the default using the ```
mex
-setup
```

command. See Change Default Compiler (MATLAB).

First, examine the `ex_kalman00`

model supplied
with the tutorial to understand the problem that you are trying to
solve using the Kalman filter.

Open the

`ex_kalman00`

model in Simulink:Set your MATLAB current folder to the folder that contains your working files for this tutorial. At the MATLAB command line, enter:

wherecd

*work*is the full path name of the folder containing your files.`work`

At the MATLAB command line, enter:

ex_kalman00

This model is an incomplete model to demonstrate how to integrate MATLAB code with Simulink. The complete model is

`ex_kalman11`

, which is also supplied with this tutorial.

**InitFcn Model Callback Function. **The model uses this callback function to:

Load position data from a MAT-file.

Set up data used by the Index generator block, which provides the second input to the Selector block.

To view this callback:

Select

**File**>**Model Properties**>**Model Properties**.Select the

**Callbacks**tab.Select

`InitFcn`

in the**Model callbacks**pane.The callback appears.

load position.mat; [R,C]=size(position); idx=(1:C)'; t=idx-1;

**Source Blocks. **The model uses two Source blocks to provide position
data and a scalar index to a Selector block.

**Selector Block. **The model uses a Selector block that selects
elements of its input signal and generates an output signal based
on its index input and its **Index Option** settings.
By changing the configuration of this block, you can generate different
size signals.

To view the Selector block settings, double-click the Selector block to view the function block parameters.

In this model, the **Index Option** for the
first port is `Select all`

and for the second
port is `Index vector (port)`

. Because the
input is a `2 x 310`

position matrix, and the index
data increments from `1`

to `310`

,
the Selector block simply outputs one `2x1`

output
at each sample time.

**MATLAB Function Block. **The model uses a MATLAB Function block to plot
the trajectory of the object and the Kalman filter estimated position.
This function:

First declares the

`figure`

,`hold`

, and`plot_trajectory`

functions as extrinsic because these MATLAB visualization functions are not supported for code generation. When you call an unsupported MATLAB function, you must declare it to be extrinsic so MATLAB can execute it, but does not try to generate code for it.Creates a figure window and holds it for the duration of the simulation. Otherwise a new figure window appears for each sample time.

Calls the

`plot_trajectory`

function, which plots the trajectory of the object and the Kalman filter estimated position.

**Simulation Stop Time. **The simulation stop time is `309`

, because
the input to the filter is a vector containing `310`

elements
and Simulink uses zero-based indexing.

To modify the model and code yourself, work through the exercises
in this section. Otherwise, open the supplied model `ex_kalman11`

in
your * solutions* subfolder to see the modified
model.

For the purposes of this tutorial, you add the MATLAB
Function block to the `ex_kalman00.mdl`

model
supplied with the tutorial. You would have to develop your own test
bench starting with an empty Simulink model.

**Adding the MATLAB Function Block. **To add a MATLAB Function block to the `ex_kalman00`

model:

Open

`ex_kalman00`

in Simulink.ex_kalman00

Add a MATLAB Function block to the model:

At the MATLAB command line, type

`slLibraryBrowser`

to open the Simulink Library Browser.From the list of Simulink libraries, select the

`User-Defined Functions`

library.Click the MATLAB Function block and drag it into the

`ex_kalman00`

model. Place the block just above the red text annotation that reads`Place MATLAB Function Block here.`

Delete the red text annotations from the model.

Save the model in the current folder as

`ex_kalman11`

.

**Calling Your MATLAB Code from the MATLAB Function Block. ** To call your MATLAB code from the MATLAB Function block:

Double-click the MATLAB Function block to open the MATLAB Function Block Editor.

Delete the default code displayed in the editor.

Copy the following code to the MATLAB Function block.

function y = kalman(u) %#codegen y = ex_kalman01(u);

Save the model.

**Connecting the MATLAB Function Block Input and Output**

Connect the MATLAB Function block input and output so that your model looks like this.

Save the model.

To simulate the model:

In the Simulink model window, select

**Simulation**>**Run**.As Simulink runs the model, it plots the trajectory of the object in blue and the Kalman filter estimated position in green. Initially, you see that it takes a short time for the estimated position to converge with the actual position of the object. Then three sudden shifts in position occur—each time the Kalman filter readjusts and tracks the object after a few iterations.

The simulation stops.

You have proved that your MATLAB algorithm works in Simulink. You are now ready to modify the filter to accept a fixed-size input, as described in Modifying the Filter to Accept a Fixed-Size Input.

The filter you have worked on so far in this tutorial uses a simple batch process that accepts one input at a time, so you must call the function repeatedly for each input. In this part of the tutorial, you learn how to modify the algorithm to accept a fixed-sized input, which makes the algorithm suitable for frame-based processing. You then modify the model to provide the input as fixed-size frames of data and call the filter passing in the data one frame at a time.

**Modifying Your MATLAB Code. **To modify the code yourself, work through the exercises in this
section. Otherwise, open the supplied file `ex_kalman03.m`

in
your

subfolder to
see the modified algorithm.*solutions*

You can now modify the algorithm to process a vector containing
more than one input. You need to find the length of the vector and
call the filter code for each element in the vector in turn. You do
this by calling the filter algorithm in a `for`

loop.

Open

`ex_kalman02.m`

in the MATLAB Editor. At the MATLAB command line, enter:edit ex_kalman02.m

Add a

`for`

loop around the filter code.Before the comment:

insert:% Predicted state and covariance

for i=1:size(z,2)

After:

insert:% Compute the estimated measurements y = H * x_est;

end

Select the code between the

`for`

statement and the end statement, right-click to open the context menu and select**Smart Indent**to indent the code.

Your filter code should now look like this:

**for i=1:size(z,2)**% Predicted state and covariance x_prd = A * x_est; p_prd = A * p_est * A' + Q; % Estimation S = H * p_prd' * H' + R; B = H * p_prd'; klm_gain = (S \ B)'; % Estimated state and covariance x_est = x_prd + klm_gain * (z - H * x_prd); p_est = p_prd - klm_gain * H * p_prd; % Compute the estimated measurements y = H * x_est;**end**Modify the line that calculates the estimated state and covariance to use the

`i`

element of input^{th}`z`

.Change:

to:x_est = x_prd + klm_gain * (z - H * x_prd);

x_est = x_prd + klm_gain * (z

**(1:2,i)**- H * x_prd);Modify the line that computes the estimated measurements to append the result to the

`i`

element of the output^{th}`y`

.Change:

to:y = H * x_est;

y

**(:,i)**= H * x_est;The code analyzer message indicator in the top right turns orange to indicate that the code analyzer has detected warnings. The code analyzer underlines the offending code in orange and places a orange marker to the right.

Move your pointer over the orange marker to view the error information.

The code analyzer detects that

`y`

must be fully defined before sub-scripting it and that you cannot grow variables through indexing in generated code.To address this warning, preallocate memory for the output

`y`

, which is the same size as the input`z`

. Add this code before the`for`

loop.% Pre-allocate output signal: y=zeros(size(z));

The orange marker disappears and the code analyzer message indicator in the top right edge of the code turns green, which indicates that you have fixed all the errors and warnings detected by the code analyzer.

Change the function name to

`ex_kalman03`

and save the file as`ex_kalman03.m`

in the current folder.

You are ready to begin the next task in the tutorial, Modifying Your Model to Call the Updated Algorithm.

**Modifying Your Model to Call the Updated Algorithm. **To modify the model yourself, work through the exercises in
this section. Otherwise, open the supplied model `ex_kalman22.mdl`

in
your * solutions* subfolder to
see the modified model.

Next, update your model to provide the input as fixed-size frames
of data and call `ex_kalman03`

passing in the data
one frame at a time.

Open

`ex_kalman11`

model in Simulink.ex_kalman11

Double-click the MATLAB Function block to open the MATLAB Function Block Editor.

Replace the code that calls

`ex_kalman02`

with a call to`ex_kalman03`

.function y = kalman(u) %#codegen

**y = ex_kalman03(u);**Close the editor.

Modify the

`InitFcn`

callback:Select

**File**>**Model Properties**>**Model Properties**.The Model Properties dialog box opens.

In this dialog box, select the

**Callbacks**tab.Select

`InitFcn`

in the**Model callbacks**pane.Replace the existing callback with:

This callback sets the frame size toload position.mat; [R,C]=size(position); FRAME_SIZE=5; idx=(1:FRAME_SIZE:C)'; LEN=length(idx); t=(1:LEN)'-1;

`5`

, and the index to increment by`5`

.Click

**Apply**and close the Model Properties dialog box.

Update the Selector block to use the correct indices.

Double-click the Selector block to view the function block parameters.

The Function Block Parameters dialog box opens.

Set the second

**Index Option**to`Starting index (port)`

.Set the

**Output Size**for the second input to`FRAME_SIZE`

, click**Apply**and close the dialog box.

Now, the

**Index Option**for the first port is`Select all`

and for the second port is`Starting index (port)`

. Because the index increments by`5`

each sample time, and the output size is`5`

, the Selector block outputs a`2x5`

output at each sample time.Change the model simulation stop time to

`61`

. Now the frame size is`5`

, so the simulation completes in a fifth of the sample times.In the Simulink model window, select

**Simulation**>**Model Configuration Parameters**.In the left pane of the Configuration Parameters dialog box, select

**Solver**.In the right pane, set

**Stop time**to`61`

.Click

**Apply**and close the dialog box.

Save the model as

`ex_kalman22.mdl`

.

**Testing Your Modified Algorithm. **To simulate the model:

In the Simulink model window, select

**Simulation**>**Run**.As Simulink runs the model, it plots the trajectory of the object in blue and the Kalman filter estimated position in green as before when you used the batch filter.

The simulation stops.

You have proved that your algorithm accepts a fixed-size signal. You are now ready for the next task, Using the Filter to Accept a Variable-Size Input.

In this part of the tutorial, you learn how to specify variable-size data in your Simulink model. Then you test your Kalman filter algorithm with variable-size inputs and see that the algorithm is suitable for processing packets of data of varying size. For more information on using variable-size data in Simulink, see Variable-Size Signal Basics.

**Updating the Model to Use Variable-Size Inputs. **To modify the model yourself, work through the exercises in
this section. Otherwise, open the supplied model `ex_kalman33.mdl`

in
your * solutions* subfolder to
see the modified model.

Open

`ex_kalman22.mdl`

in Simulink.ex_kalman22

Modify the

`InitFcn`

callback:Select

**File**>**Model Properties**>**Model Properties**.The Model Properties dialog box opens.

Select the

**Callbacks**tab.Select

`InitFcn`

in the**Model callbacks**pane.Replace the existing callback with:

This callback sets up indexing to generate eleven different size inputs. It specifies the start and end indices for each sample time. The first sample time uses only the first element, the second sample time uses the second and third elements, and so on. The largest sample,load position.mat; idx=[ 1 1 ;2 3 ;4 6 ;7 10 ;11 15 ;16 30 ; 31 70 ;71 100 ;101 200 ;201 250 ;251 310]; LEN=length(idx); t=(0:1:LEN-1)';

`101`

to`200`

, contains 100 elements.Click

**Apply**and close the**Model Properties**dialog box.

Update the Selector block to use the correct indices.

Double-click the Selector block to view the function block parameters.

The Function Block Parameters dialog box opens.

Set the second

**Index Option**to`Starting and ending indices (port)`

, then click**Apply**and close the dialog box.This setting means that the input to the index port specifies the start and end indices for the input at each sample time. Because the index input specifies different starting and ending indices at each sample time, the Selector block outputs a variable-size signal as the simulation progresses.

Use the Ports and Data Manager to set the MATLAB Function input

`x`

and output`y`

as variable-size data.Double-click the MATLAB Function block to open the MATLAB Function Block Editor.

From the editor menu, select

**Edit Data**.In the Ports and Data Manager left pane, select the input

`u`

.The Ports and Data Manager displays information about

`u`

in the right pane.On the

**General**tab, select the**Variable size**check box and click**Apply**.In the left pane, select the output

`y`

.On the

**General**tab:Set the

**Size**of`y`

to`[2 100]`

to specify a 2-D matrix where the upper bounds are`2`

for the first dimension and`100`

for the second, which is the maximum size input specified in the`InitFcn`

callback.Select the

**Variable size**check box.Click

**Apply**.

Close the Ports and Data Manager.

Now do the same for the other MATLAB Function block. Use the Ports and Data Manager to set the Visualizing block inputs

`y`

and`z`

as variable-size data.Double-click the Visualizing block to open the MATLAB Function Block Editor.

From the editor menu, select

**Edit Data**.In the Ports and Data Manager left pane, select the input

`y`

.On the

**General**tab, select the**Variable size**check box and click**Apply**.In the left pane, select the input

`z`

.On the

**General**tab, select the**Variable size**check box and click**Apply**.Close the Ports and Data Manager.

Change the model simulation stop time to

`10`

. This time, the filter processes one of the eleven different size inputs each sample time.Save the model as

`ex_kalman33.mdl`

.

**Testing Your Modified Model. **To simulate the model:

In the Simulink model window, select

**Simulation**>**Run**.As Simulink runs the model, it plots the trajectory of the object in blue and the Kalman filter estimated position in green as before.

Note that the signal lines between the Selector block and the Tracking and Visualization blocks change to show that these signals are variable-size.

The simulation stops.

You have successfully created an algorithm that accepts variable-size inputs. Next, you learn how to debug your MATLAB Function block, as described in Debugging the MATLAB Function Block.

You can debug your MATLAB Function block just like you can debug a function in MATLAB.

Double-click the MATLAB Function block that calls the Kalman filter to open the MATLAB Function Block Editor.

In the editor, click the dash (-) character in the left margin of the line:

y = kalman03(u);

A small red ball appears in the margin of this line, indicating that you have set a breakpoint.

In the Simulink model window, select

**Simulation**>**Run**.The simulation pauses when execution reaches the breakpoint and a small green arrow appears in the left margin.

Place the pointer over the variable

`u`

.The value of

`u`

appears adjacent to the pointer.From the MATLAB Function Block Editor menu, select

**Step In**.The

`kalman03.m`

file opens in the editor and you can now step through this code using**Step**,**Step In**, and**Step Out**.Select

**Step Out**.The

`kalman03.m`

file closes and the MATLAB Function block code reappears in the editor.Place the pointer over the output variable

`y`

.You can now see the value of

`y`

.Click the red ball to remove the breakpoint.

From the MATLAB Function Block Editor menu, select

**Quit Debugging**.Close the editor.

Close the figure window.

Now you are ready for the next task, Generating C Code.

You have proved that your algorithm works in Simulink. Next you generate code for your model.

Before generating code, you must check that your MATLAB code is suitable for code generation. If you call your MATLAB code as an extrinsic function, you must remove extrinsic calls before generating code.

Rename the MATLAB Function block to

`Tracking`

. To rename the block, double-click the annotation`MATLAB Function`

below the MATLAB Function block and replace the text with`Tracking`

.When you generate code for the MATLAB Function block, Simulink Coder uses the name of the block in the generated code. It is good practice to use a meaningful name.

Before generating code, ensure that Simulink Coder creates a code generation report. This HTML report provides easy access to the list of generated files with a summary of the configuration settings used to generate the code.

In the Simulink model window, select

**Simulation**>**Model Configuration Parameters**.The Configuration Parameters dialog box opens.

In the left pane of the Configuration Parameters dialog box, select

**Report**under**Code Generation**.In the right pane, select

**Create code generation report**.The

**Open report automatically**option is also selected.Click

**Apply**and close the Configuration Parameters dialog box.Save your model.

To generate code for the Tracking block:

In your model, select the Tracking block.

In the Simulink model window, select

**Code**>**C/C++ Code**>**Build Selected Subsystem**.

The Simulink software generates an error informing you that it cannot log variable-size signals as arrays. You need to change the format of data saved to the MATLAB workspace. To change this format:

In the Simulink model window, select

**Simulation**>**Model Configuration Parameters**.The Configuration Parameters dialog box opens.

In the left pane of the Configuration Parameters dialog box, select

**Data Import/Export**and set the**Format**to`Structure with time`

.The logged data is now a structure that has two fields: a time field and a signals field, enabling Simulink to log variable-size signals.

Click

**Apply**and close the Configuration Parameters dialog box.Save your model.

Repeat step

`3`

to generate code for the Tracking block.The Simulink Coder software generates C code for the block and launches the code generation report.

For more information on using the code generation report, see Reports for Code Generation (Simulink Coder).

In the left pane of the code generation report, click the

`Tracking.c`

link to view the generated C code. Note that in the code generated for the MATLAB Function block,`Tracking`

, there is no code for the`ex_kalman03`

function because inlining is enabled by default.Modify your filter algorithm to disable inlining:

In

`ex_kalman03.m`

, after the function declaration, add:coder.inline('never');

Change the function name to

`ex_kalman04`

and save the file as`ex_kalman04.m`

in the current folder.In your

`ex_kalman33`

model, double-click the Tracking block.The MATLAB Function Block Editor opens.

Modify the call to the filter algorithm to call

`ex_kalman04`

.function y = kalman(u) %#codegen

**y = ex_kalman04(u);**Save the model as

`ex_kalman44.mdl`

.

Generate code for the updated model.

Select the Tracking block.

In the model window, select

**Code**>**C/C++Code**>**Build Selected Subsystem**.The

**Build code for Subsystem**dialog box appears.Click the

**Build**button.The Simulink Coder software generates C code for the block and launches the code generation report.

In the left pane of the code generation report, click the

`Tracking.c`

link to view the generated C code.This time the

`ex_kalman04`

function has code because you disabled inlining./* Forward declaration for local functions */ static void Tracking_ex_kalman04(const real_T z_data[620], const int32_T z_sizes[2], real_T y_data[620], int32_T y_sizes[2]); /* Function for MATLAB Function Block: '<Root>/Tracking' */ static void Tracking_ex_kalman04(const real_T z_data[620], const int32_T 48 z_sizes[2], real_T y_data[620], int32_T y_sizes[2])

Back up your MATLAB code before you modify it.

Decide on a naming convention for your files and save interim versions frequently. For example, this tutorial uses a two-digit suffix to differentiate the various versions of the filter algorithm.

For simulation purposes, before generating code, call your MATLAB code using

`coder.extrinsic`

to check that your algorithm is suitable for use in Simulink. This practice provides these benefits:You do not have to make the MATLAB code suitable for code generation.

You can debug your MATLAB code in MATLAB while calling it from Simulink.

Create a Simulink Coder code generation report. This HTML report provides easy access to the list of generated files with a summary of the configuration settings used to generate the code.

- Filter Audio Signal Using MATLAB Code
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