Filter Design Toolbox

adaptfilt.lms

Construct a least-mean-square (LMS) FIR adaptive filter object

Syntax

• ha = adaptfilt.lms(l,step,leakage,coeffs,states)
  

Description

ha = adaptfilt.lms(l,step,leakage,coeffs,states) constructs an FIR LMS adaptive filter object ha.

Input Arguments

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

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 0.1.
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.
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.

adaptfilt.lms Object Properties

In the syntax for creating the adaptfilt object, the input options are properties of the object created. This table list all the properties for the adaptfilt.lms object, their default values, and a brief description of the property.

Property	Range	Property Description
Algorithm	None	Reports the adaptive filter algorithm the object uses during adaptation
FilterLength	Any positive integer	Reports the length of the filter, the number of coefficients or taps
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.
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 - 1).
StepSize		NLMS step size. It must be a scalar between zero and one. Setting this step size value to one provides the fastest convergence. step defaults to 0.1.
Leakage		NLMS leakage factor. It must be a scalar between zero and one. When it is less than one, a leaky NLMS algorithm results. leakage defaults to 1 (no leakage).
ResetBeforeFiltering	off or on	Determine whether the filter states and coefficients get restored to their starting values for each filtering operation. The starting values are the values in place when you create the filter. ResetBeforeFiltering returns to zero any property value that the filter changes during processing. Property values that the filter does not change are not affected. Defaults to 'on'.
NumSamplesProcessed	Any integer	Returns the number of samples processed during filtering. Defaults to zero.

Example

Use 500 iterations of an adapting filter system to identify and unknown 32nd-order FIR filter.

• 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;
  

ISee Also

adaptfilt.blms, adaptfilt.blmsfft, adaptfilt.dlms, adaptfilt.nlms, adaptfilt.tdafdft, adaptfilt.sd, adaptfilt.se, adaptfilt.ss

Reference

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

adaptfilt.hswrls

adaptfilt.lsl