Filter Design Toolbox

adaptfilt

Construct an adaptive filter object

Syntax

• ha = adaptfilt.algorithm(input1,input2,...)
  

Description

ha = adaptfilt.algorithm('input1',input2,...) returns the adaptive filter object ha that uses the adaptive filtering technique specified by algorithm. When you construct an adaptive filter object, include an algorithm specifier to implement a specific adaptive filter. Note that you do not enclose the algorithm option in single quotation marks as you do for most strings. To construct an adaptive filter object you must supply an algorithm string--there is no default algorithm, although every constructor creates a default adaptive filter when you do not provide input arguments such as input1 or input2 in the calling syntax.

Algorithms

For adaptive filter (adaptfilt) objects, the algorithm string determines which adaptive filter algorithm your adaptfilt object implements. Each available algorithm entry appears in one of the tables along with a brief description of the algorithm. Click on the algorithm in the first column to get more information about the associated adaptive filter technique.

• LMS based adaptive filters
• RLS based adaptive filters
• Affine projection adaptive filters
• Adaptive filters in the frequency domain
• Lattice based adaptive filters

Least Mean Squares (LMS) Based FIR Adaptive Filters

adaptfilt.algorithm String	Description of the Adapting Algorithm Used to Generate Filter Coefficients During Adaptation
adaptfilt.adjlms	Use the Adjoint LMS FIR adaptive filter algorithm
adaptfilt.blms	Use the Block LMS FIR adaptive filter algorithm
adaptfilt.blmsfft	Use the FFT-based Block LMS FIR adaptive filter algorithm
adaptfilt.dlms	Use the delayed LMS FIR adaptive filter algorithm
adaptfilt.filtxlms	Use the filtered-x LMS FIR adaptive filter algorithm
adaptfilt.lms	Use the LMS FIR adaptive filter algorithm
adaptfilt.nlms	Use the normalized LMS FIR adaptive filter algorithm
adaptfilt.sd	Use the sign-data LMS FIR adaptive filter algorithm
adaptfilt.se	Use the sign-error LMS FIR adaptive filter algorithm
adaptfilt.ss	Use the sign-sign LMS FIR adaptive filter algorithm

For further information about an adapting algorithm, refer to the reference page for the algorithm.

Recursive Least Squares (RLS) Based FIR Adaptive Filters

adaptfilt.algorithm String	Description of the Adapting Algorithm Used to Generate Filter Coefficients During Adaptation
adaptfilt.ftf	Use the fast transversal least squares adaptation algorithm
adaptfilt.qrdrls	Use the QR-decomposition RLS adaptation algorithm
adaptfilt.hrls	Use the householder RLS adaptation algorithm
adaptfilt.hswrls	Use the householder SWRLS adaptation algorithm
adaptfilt.rls	Use the recursive-least squares (RLS) adaptation algorithm
adaptfilt.swrls	Use the sliding window (SW) RLS adaptation algorithm
adaptfilt.swftf	Use the sliding window FTF adaptation algorithm

For more complete information about an adapting algorithm, refer to the reference page for the algorithm.

Affine Projection (AP) FIR Adaptive Filters

adaptfilt.algorithm String	Description of the Adapting Algorithm Used to Generate Filter Coefficients During Adaptation
adaptfilt.ap	Use the affine projection algorithm that uses direct matrix inversion
adaptfilt.apru	Use the affine projection algorithm that uses recursive matrix updating
adaptfilt.bap	Use the block affine projection adaptation algorithm

To find more information about an adapting algorithm, refer to the reference page for the algorithm.

FIR Adaptive Filters in the Frequency Domain (FD)

adaptfilt.algorithm String	Description of the Adapting Algorithm Used to Generate Filter Coefficients During Adaptation
adaptfilt.fdaf	Use the frequency domain adaptation algorithm
adaptfilt.pbfdaf	Use the partition block version of the FDAF algorithm
adaptfilt.pbufdaf	Use the partition block unconstrained version of the FDAF algorithm
adaptfilt.tdafdct	Use the transform domain adaptation algorithm using DCT
adaptfilt.tdafdft	Use the transform domain adaptation algorithm using DFT
adaptfilt.ufdaf	Use the unconstrained FDAF algorithm for adaptation

For more information about an adapting algorithm, refer to the reference page for the algorithm.

Lattice Based (L) FIR Adaptive Filters

adaptfilt.algorithm String	Description of the Adapting Algorithm Used to Generate Filter Coefficients During Adaptation
adaptfilt.gal	Use the gradient adaptive lattice filter adaptation algorithm
adaptfilt.lsl	Use the least squares lattice adaptation algorithm
adaptfilt.qrdlsl	Use the QR decomposition least squares lattice adaptation algorithm

For more information about an adapting algorithm, refer to the reference page for the algorithm.

Properties for all Adaptive Filter Objects

Each reference page for an algorithm and adaptfilt.algorithm object specifies which properties apply to the adapting algorithm and how to use them.

Methods for Adaptive Filter Objects

As is true with all objects, methods enable you to perform various operations on adaptfilt objects. To use the methods, you apply them to the object handle that you assigned when you constructed the adaptfilt object.

Most of the analysis methods that apply to dfilt objects also work with adaptfilt objects. Methods like freqz rely on the filter coefficients in the adaptfilt object. Since the coefficients change each time the filter adapts to data, you should view the results of using a method as an analysis of the filter at a moment in time for the object. Use caution when you apply an analysis method to your adaptive filter objects--always check that your result approached your expectation.

In particular, the Filter Visualization Tool (FVTool) supports all of the adaptfilt objects. Analyzing and viewing your adaptfilt objects is straightforward--use the fvtool method with the name of your object

• fvtool(objectname)
  

to lauch FVTool and work with your object.

Some methods share their names with functions in the Signal Processing Toolbox, or even functions in this toolbox. Functions that share names with methods behave in a similar way. Using the same name for more than one function or method is called overloading and is common is many toolboxes.

Method	Description
adaptfilt/coefficients	Return the instantaneous adaptive filter coefficients
adaptfilt/filter	Apply an adaptfilt object to your signal
adaptfilt/freqz	Plot the instantaneous adaptive filter frequency response
adaptfilt/grpdelay	Plot the instantaneous adaptive filter group delay
adaptfilt/impz	Plot the instantaneous adaptive filter impulse response.
adaptfilt/info	Return the adaptive filter information.
adaptfilt/isfir	Test whether an adaptive filter is an finite impulse response (FIR) filters.
adaptfilt/islinphase	Test whether an adaptive filter is linear phase
adaptfilt/ismaxphase	Test whether an adaptive filter is maximum phase
adaptfilt/isminphase	Test whether an adaptive filter is minimum phase
adaptfilt/isreal	True whether an adaptive filter has real coefficients
adaptfilt/isstable	Test whether an adaptive filter is stable
adaptfilt/maxstep	Return the maximum step size for an adaptive filter
adaptfilt/msepred	Return the predicted mean square error
adaptfilt/msesim	Return the measured mean square error via simulation.
adaptfilt/phasez	Plot the instantaneous adaptive filter phase response
adaptfilt/reset	Reset an adaptive filter to initial conditions
adaptfilt/stepz	Plot the instantaneous adaptive filter step response
adaptfilt/tf	Return the instantaneous adaptive filter transfer function
adaptfilt/zerophase	Plot the instantaneous adaptive filter zerophase response
adaptfilt/zpk	Return a matrix containing the instantaneous adaptive filter zero, pole, and gain values
adaptfilt/zplane	Plot the instantaneous adaptive filter in the Z-plane

Working with Adaptive Filter Objects

The next sections cover viewing and changing the properties of adaptfilt objects. Generally, modifying the properties is the same for adaptfit, dfilt, mfilt, and qfilt objects and most of the same methods apply to all.

Viewing Object Properties

As with any object, you can use get to view a adaptfilt object's properties. To see a specific property, use

•  get(ha,'property') 
  

To see all properties for an object, use

• get(ha)
  

Changing Object Properties

To set specific properties, use

• set(ha,'property1',value1,'property2',value2,...) 
  

You must use single quotation marks around the property name so MATLAB treats them as strings.

Copying an Object

To create a copy of an object, use copy.

• ha2 = copy(ha)
  

Note    Using the syntax ha2 = ha copies only the object handle and does not create a new object. When you change the characteristics of ha2, those of ha change as well--ha and ha2 are not independent.

Using Filter States

Two properties control your adaptive filter states.

• States--stores the current states of the filter. Before the filter is applied, the states correspond to the initial conditions and after the filter is applied, the states correspond to the final conditions.
• ResetBeforeFiltering--resets the filter before filtering. The default value is 'on' which causes the properties that are modified by the filter, such as coefficients and states, to be reset to the value you specified when you constructed the object, before you use the object to filter data. Setting ResetBeforeFiltering to 'off' allows the object to retain its current properties between filtering operations, rather than resetting the filter to its property values at construction.

Examples

Construct an LMS adaptive filter object and use it to identify an unknown system. For this example, use 500 iteration of the adapting process to determine the unknown filter coefficients. Using the LMS algorithm represents one of the most straightforward technique for adaptive filters.

• x  = randn(1,500);     % Input to the filter
  b  = fir1(31,0.5);     % FIR system to be identified
  n  = 0.1*randn(1,500); % Observation noise signal
  d  = filter(b,1,x)+n;  % Desired signal
  mu = 0.008;            % LMS step size.
  ha = adaptfilt.lms(32,mu);
  [y,e] = filter(ha,x,d);
  subplot(2,1,1); plot(1:500,[d;y;e]);
  title('System Identification of an FIR Filter');
  legend('Desired','Output','Error');
  xlabel('Time Index'); ylabel('Signal Value');
  subplot(2,1,2); stem([b.',ha.coefficients.']);
  legend('Actual','Estimated');
  xlabel('Coefficient #'); ylabel('Coefficient Value'); grid on;
  

Glancing at the figure shows you the coefficients after adapting closely match the desired unknown FIR filter.

See Also

dfilt, filter, mfilt

Functions--Alphabetical List

adaptfilt.adjlms