Filter Design Toolbox

adaptfilt.dlms

Create a delayed LMS FIR adaptive filter object

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.

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	Vvector 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.

adaptfilt.dlms Object Properties

In the syntax for creating the adaptfilt object, the input options are properties of the object you create. This table list all 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
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. 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
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).
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.
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.
ResetBeforeFiltering	off or on	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. ResetBeforeFiltering returns to zero any state that the filter changes during processing. States that the filter does not change are not affected. Defaults to 'on'.
NumSamplesProcessed	Any integer	Returns the number of samples processed during filtering. As a check, the number of samples reported processed plus the number of nonprocessed samples should be the total number of input samples. Defaults to zero.

Example

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'); 
  xlabel('Coefficient #'); ylabel('Coefficient Value'); grid on;
  

See Also

adaptfilt.adjlms, adaptfilt.filtxlms, adaptfilt.lms

Reference

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

adaptfilt.blmsfft

adaptfilt.fdaf