FIR adaptive filter that uses sign-sign algorithm
adaptfilt.ss has been removed. Use
ha = adaptfilt.ss(l,step,leakage,coeffs,states)
ha = adaptfilt.ss(l,step,leakage,coeffs,states) constructs
an FIR sign-error adaptive filter
For information on how to run data through your adaptive filter
object, see the Adaptive Filter Syntaxes section of the reference
Entries in the following table describe the input arguments
Adaptive filter length (the number of coefficients or
taps) and it must be a positive integer.
SS step size. It must be a nonnegative scalar.
Your SS 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
adaptfilt.ss can be called for a block of
d are vectors,
or in "sample by sample mode" using a For-loop with
the method filter:
for n = 1:length(x) ha = adaptfilt.ss(25,0.9); [y(n),e(n)] = filter(ha,(x(n),d(n),s)); end
In the syntax for creating the
most of the input options are properties of the object you create.
This table lists the properties for sign-sign objects, their default
values, and a brief description of the property.
Defines the adaptive filter algorithm the object uses during adaptation
Vector containing the initial filter coefficients. It
must be a length
Reports the length of the filter, the number of coefficients or taps
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. 1 is the default value.
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.
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.
Demonstrating adaptive line enhancement using a 32-coefficient FIR filter provides a good introduction to the sign-sign algorithm.
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; % Sign-error step size ha = adaptfilt.ss(32,mu); [y,e] = filter(ha,x,d); subplot(2,1,1); plot(1:ntr,[d;y;v(1:end-1)]); axis([ntr-100 ntr -3 3]); title('Adaptive Line Enhancement of Noisy Sinusoid'); legend('Observed','Enhanced','Original'); xlabel('Time Index'); ylabel('Signal Value'); [InputPsd,wi] = pwelch(x(ntr-1000:ntr)); [OutputPsd,wo] = pwelch(y(ntr-1000:ntr)); CompPsdEst = [InputPsd/max(InputPsd), OutputPsd/max(OutputPsd)]; subplot(2,1,2); plot(wi/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;
This example is the same as the ones used for the sign-data and sign-error examples. Comparing the figures shown for each of the others lets you assess the performance of each for the same task.
Lucky, R.W, "Techniques For Adaptive Equalization of Digital Communication Systems," Bell Systems Technical Journal, vol. 45, pp. 255-286, Feb. 1966
Hayes, M., Statistical Digital Signal Processing and Modeling, New York, Wiley, 1996.