FIR adaptive filter that uses window recursive least squares (RLS)
adaptfilt.swrls
has been removed. Use dsp.RLSFilter
instead.
ha = adaptfilt.swrls(l,lambda,invcov,swblocklen,
dstates,...coeffs,states)
ha = adaptfilt.swrls(l,lambda,invcov,swblocklen,
constructs an FIR sliding
window RLS adaptive filter
dstates,...coeffs,states)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
.
Entries in the following table describe the input arguments
for adaptfilt.swrls
.
Input Argument  Description 

 Adaptive filter length (the number of coefficients or
taps). It must be a positive integer. 
 RLS forgetting factor. This is a scalar and should lie
within the range (0, 1]. 
 Inverse of the input signal covariance matrix. You should
initialize 
 Block length of the sliding window. This integer must
be at least as large as the filter length. 
 Desired signal states of the adaptive filter. 
 Vector of initial filter coefficients. It must be a length 
 Vector of initial filter states. 
Since your adaptfilt.swrls
filter is an object,
it has properties that define its behavior in operation. Note that
many of the properties are also input arguments for creating adaptfilt.swrls
objects.
To show you the properties that apply, this table lists and describes
each property for the filter object.
Name  Range  Description 

 None  Defines the adaptive filter algorithm the object uses during adaptation 
 Any vector of  Vector containing the initial filter coefficients. It
must be a length 
 Vector  Desired signal states of the adaptive filter. 
 Any positive integer  Reports the length of the filter, the number of coefficients or taps 
 Scalar  Forgetting factor of the adaptive filter. This is a
scalar and should lie in the range (0, 1]. It defaults to 1. Setting 
 Matrix  Square matrix with each dimension equal to the filter
length 
 Vector with dimensions (  Empty when you construct the object, this gets populated after you run the filter. 

 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. 
 Vector of elements, data type double  Vector of the adaptive filter states. 
 Integer  Block length of the sliding window. This integer must
be at least as large as the filter length. 
System Identification of a 32coefficient FIR filter. Use 500 iterations to adapt to the unknown filter. After the example code, you see a figure that plots the results of the running the code.
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 P0 = 10*eye(32); % Initial correlation matrix inverse lam = 0.99; % RLS forgetting factor N = 64; % Block length ha = adaptfilt.swrls(32,lam,P0,N); [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;
In the figure you see clearly that the adaptive filter process successfully identified the coefficients of the unknown FIR filter. You knew it had to or many things that you take for granted, such as modems on computers, would not work.