FIR adaptive filter that uses signerror algorithm
adaptfilt.se
has been removed. Use dsp.LMSFilter
instead.
ha = adaptfilt.se(l,step,leakage,coeffs,states)
ha = adaptfilt.se(l,step,leakage,coeffs,states)
constructs
an FIR signerror 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
.
Entries in the following table describe the input arguments
for adaptfilt.se
.
Input Argument  Description 

 Adaptive filter length (the number of coefficients or
taps) and it must be a positive integer. 
 SE step size. It must be a nonnegative scalar. You can
use 
 Your SE leakage factor. It must be a scalar between 0
and 1. When 
 Vector of initial filter coefficients. it must be a length 
 Vector of initial filter states for the adaptive filter.
It must be a length 
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 signerror SD object, their default values,
and a brief description of the property.
Property  Default Value  Description 

 Signerror  Defines the adaptive filter algorithm the object uses during adaptation 

 Vector containing the initial filter coefficients. It
must be a length 
 10  Reports the length of the filter, the number of coefficients or taps 
 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. Defaults to one if omitted. 

 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. 

 Vector of the adaptive filter states. 
 0.1  Sets the SE 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. 
Use inspect(ha)
to view or change the object
properties graphically using the MATLAB Property Inspector.
Adaptive line enhancement using a 32coefficient FIR filter running over 5000 iterations.
d = 1; % Number of samples of delay ntr= 5000; % Number of iterations v = sin(2*pi*0.05*(1:ntr+d)); % Sinusoidal signal n = randn(1,ntr+d); % Noise signal x = v(1:ntr)+n(1:ntr); % Input signal (delayed desired signal) d = v(1+d:ntr+d)+n(1+d:ntr+d); % Desired signal mu = 0.0001; % Signerror step size ha = adaptfilt.se(32,mu); [y,e] = filter(ha,x,d); subplot(2,1,1); plot(1:ntr,[d;y;v(1:end1)]); axis([ntr100 ntr 3 3]); title('Adaptive Line Enhancement of Noisy Sinusoid'); legend('Observed','Enhanced','Original'); xlabel('Time Index'); ylabel('Signal Value'); HWelch = spectrum.welch; InputPsd = psd(HWelch,x(ntr1000:ntr)); OutputPsd = psd(HWelch,y(ntr1000:ntr)); CompPsdEst = [InputPsd.Data/max(InputPsd.Data), OutputPsd.Data/max(OutputPsd.Data)]; subplot(2,1,2); plot(InputPsd.Frequencies/pi,10*log10(CompPsdEst)); axis([0 1 60 20]); legend('Observed','Enhanced'); xlabel('Normalized Frequency (\times \pi rad/sample)'); ylabel('Power Spectral Density'); grid on;
Compare the figure shown here to the ones for adaptfilt.sd
and adaptfilt.ss
to
see how the variants perform on the same example.
Gersho, A, "Adaptive Filtering With Binary Reinforcement," IEEE^{®} Trans. Information Theory, vol. IT30, pp. 191199, March 1984.
Hayes, M, Statistical Digital Signal Processing and Modeling, New York, Wiley, 1996.