FIR adaptive filter that uses sign-sign algorithm
ha = adaptfilt.ss(l,step,leakage,coeffs,states)
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.ss.
Adaptive filter length (the number of coefficients or taps) and it must be a positive integer. l defaults to 10.
SS step size. It must be a nonnegative scalar. step defaults to 0.1.
Your SS leakage factor. It must be a scalar between 0 and 1. When leakage is less than one, adaptfilt.lms implements a leaky SS algorithm. When you omit the leakage property in the calling syntax, it defaults to 1 providing no leakage in the adapting algorithm.
Vector of initial filter coefficients. it must be a length l vector. coeffs defaults to length l vector with elements equal to zero.
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.ss can be called for a block of data, when x and 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 adaptfilt object, 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 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. Should be initialized with the initial coefficients for the FIR filter prior to adapting.
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.
false or true
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. PersistentMemory returns to zero any property value that the filter changes during processing. Property values that the filter does not change are not affected. Defaults to false.
Vector of the adaptive filter states. states defaults to a vector of zeros which has length equal to (l-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.
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.