Filter Design Toolbox

adaptfilt.filtxlms

Create an filtered-x LMS FIR adaptive filter

Syntax

• ha = adaptfilt.filtxlms(l,step,leakage,pathcoeffs,pathest,...
  errstates,pstates,coeffs,states)
  

Description

ha = adaptfilt.filtxlms(l,step,leakage,pathcoeffs,pathest,...
errstates,pstates,coeffs,states) constructs an filtered-x LMS adaptive filter ha.

Input Arguments

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

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	Filtered LMS step size. it must be a nonnegative scalar. step defaults to 0.1.
leakage	is the filtered-x LMS leakage factor. it must be a scalar between 0 and 1. If it is less than one, a leaky version of adaptfilt.filtxlms is implemented. leakage defaults to 1 (no leakage).
pathcoeffs	is the secondary path filter model. this vector should contain the coefficient values of the secondary path from the output actuator to the error sensor.
pathest	is the estimate of the secondary path filter model. pathest defaults to the values in pathcoeffs.
fstates	is a vector of filtered input states of the adaptive filter. fstates defaults to a zero vector of length equal to (l - 1).
pstates	are the secondary path FIR filter states. it must be a vector of length equal to the (length(pathcoeffs) - 1). pstates defaults to a vector of zeros of appropriate length.
coeffs	is a vector of initial filter coefficients. it must be a length l vector. coeffs defaults to length l vector of zeros.
states	Vector of initial filter states. states defaults to a zero vector of length equal to the larger of (length(pathcoeffs) - 1) and (length(pathest) - 1).

adaptfilt.filtxlms 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 adjoint 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.
States	Vector of elements	Vector of the adaptive filter states. states defaults to a vector of zeros which has length equal to (l + projectord - 2)
SecondaryPathCoeffs	No default	A vector that contains the coefficient values of your secondary path from the output actuator to the error sensor
SecondaryPathEstimate	pathcoeffs values	An estimate of the secondary path filter model
SecondaryPathStates	Vector of size (length(pathcoeffs)-1) with all elements equal to zero.	The states of the secondary path FIR filter--the unknown system
FilteredInputStates	l-1	Vector of filtered input states with lenght equal to l - 1.
StepSize	0.1	Sets the filtered-x 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.

Example

Demonstrate active noise control of a random noise signal over 1000 iterations.

• x  = randn(1,1000);      % Noise source
  g  = fir1(47,0.4);       % FIR primary path system model
  n  = 0.1*randn(1,1000);  % Observation noise signal
  d  = filter(g,1,x)+n;    % Signal to be cancelled (desired)
  b  = fir1(31,0.5);       % FIR secondary path system model 
  mu = 0.008;              % Filtered-X LMS step size
  ha = adaptfilt.filtxlms(32,mu,1,b);
  [y,e] = filter(ha,x,d);
  plot(1:1000,d,'b',1:1000,e,'r');
  title('Active Noise Control of a Random Noise Signal');
  legend('Original','Attenuated');
  xlabel('Time Index'); ylabel('Signal Value');  grid on;
  

See also

adaptfilt.dlms, 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.fdaf

adaptfilt.ftf