Contents

adaptfilt.dlms

FIR adaptive filter that uses delayed LMS

Syntax

ha = adaptfilt.dlms(l,step,leakage,delay,errstates,coeffs,
...states)

Description

ha = adaptfilt.dlms(l,step,leakage,delay,errstates,coeffs,
...states)
constructs an FIR delayed LMS adaptive filter ha.

For information on how to run data through your adaptive filter object, see the Adaptive Filter Syntaxes section of the reference page for filter.

Input Arguments

Entries in the following table describe the input arguments for adaptfilt.dlms.

Input Argument

Description

l

Adaptive filter length (the number of coefficients or taps) and it must be a positive integer. l defaults to 10.

step

LMS step size. It must be a nonnegative scalar. You can use maxstep to determine a reasonable range of step size values for the signals being processed. step defaults to 0.

leakage

Your LMS leakage factor. It must be a scalar between 0 and 1. When leakage is less than one, adaptfilt.lms implements a leaky LMS algorithm. When you omit the leakage property in the calling syntax, it defaults to 1 providing no leakage in the adapting algorithm.

delay

Update delay given in time samples. This scalar should be a positive integer — negative delays do not work. delay defaults to 1.

errstates

Vector of the error states of your adaptive filter. It must have a length equal to the update delay (delay) in samples. errstates defaults to an appropriate length vector of zeros.

coeffs

Vector of initial filter coefficients. it must be a length l vector. coeffs defaults to length l vector with elements equal to zero.

states

Vector of initial filter states for the adaptive filter. It must be a length l-1 vector. states defaults to a length l-1 vector of zeros.

Properties

In the syntax for creating the adaptfilt object, the input options are properties of the object you create. This table lists the properties for the block LMS object, their default values, and a brief description of the property.

Property

Default Value

Description

Algorithm

None

Defines the adaptive filter algorithm the object uses during adaptation

Coefficients

Vector of elements

Vector containing the initial filter coefficients. It must be a length l vector where l is the number of filter coefficients. coeffs defaults to length l vector of zeros when you do not provide the argument for input. LMS FIR filter coefficients. Should be initialized with the initial coefficients for the FIR filter prior to adapting. You need l entries in coeffs.

Delay

1

Specifies the update delay for the adaptive algorithm.

ErrorStates

Vector of zeros with the number of elements equal to delay

A vector comprising the error states for the adaptive filter.

FilterLength

Any positive integer

Reports the length of the filter, the number of coefficients or taps.

Leakage

1

Specifies the leakage parameter. Allows you to implement a leaky algorithm. Including a leakage factor can improve the results of the algorithm by forcing the algorithm to continue to adapt even after it reaches a minimum value. Ranges between 0 and 1.

PersistentMemory

false or true

Determine whether the filter states get restored to their starting values for each filtering operation. The starting values are the values in place when you create the filter if you have not changed the filter since you constructed it. PersistentMemory returns to zero any state that the filter changes during processing. States that the filter does not change are not affected. Defaults to false.

StepSize

0.1

Sets the LMS algorithm step size used for each iteration of the adapting algorithm. Determines both how quickly and how closely the adaptive filter converges to the filter solution.

States

Vector of elements, data type double

Vector of the adaptive filter states. states defaults to a vector of zeros which has length equal to (l + projectord - 2).

Examples

System identification of a 32-coefficient FIR filter. Refer to the figure that follows to see the results of the adapting filter process.

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.
delay = 1;              % Update delay
ha = adaptfilt.dlms(32,mu,1,delay);
[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'); grid on;
xlabel('Coefficient #'); ylabel('Coefficient Value');

Using a delayed LMS adaptive filter in the process to identify an unknown filter appears to work as planned, as shown in this figure.

References

Shynk, J.J.,"Frequency-Domain and Multirate Adaptive Filtering," IEEE® Signal Processing Magazine, vol. 9, no. 1, pp. 14-37, Jan. 1992.

Was this topic helpful?