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Sign-data FIR adaptive filter has been removed. Use dsp.LMSFilter instead.


ha =,step,leakage,coeffs,states)


ha =,step,leakage,coeffs,states) constructs an FIR sign-data adaptive filter object 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.

Input Arguments

Entries in the following table describe the input arguments for

Input Argument



Adaptive filter length (the number of coefficients or taps) and it must be a positive integer. l defaults to 10.


SD step size. It must be a nonnegative scalar. step defaults to 0.1


Your SD leakage factor. It must be a scalar between 0 and 1. When leakage is less than one, implements a leaky SD 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.


In the syntax for creating the adaptfilt object, the input options are properties of the object you create. This table lists the properties for sign-data objects, their default values, and a brief description of the property.


Default Value


Al gorithm


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. You need l entries in coefficients.



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. Defaults to 0.


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 SD 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.


Adaptive line enhancement using a 32-coefficient FIR filter to perform the enhancement. This example runs for 5000 iterations, as you see in property iter.

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
d  = v(1+d:ntr+d)+n(1+d:ntr+d); % Desired signal
mu = 0.0001;                    % Sign-data step size.
ha =,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 a Noisy Sinusoidal Signal');
 xlabel('Time Index'); ylabel('Signal Value');
[pxx,om] = pwelch(x(ntr-1000:ntr));
 pyy = pwelch(y(ntr-1000:ntr));
axis([0 1 -60 20]); legend('Observed','Enhanced');
xlabel('Normalized Frequency (\times \pi rad/sample)');
ylabel('Power Spectral Density'); grid on;

Each of the variants — sign-data, sign-error, and sign-sign — uses the same example. You can compare the results by viewing the figure shown for each adaptive filter method —,, and


Moschner, J.L., "Adaptive Filter with Clipped Input Data," Ph.D. thesis, Stanford Univ., Stanford, CA, June 1970.

Hayes, M., Statistical Digital Signal Processing and Modeling, New York Wiley, 1996.

Introduced in R2011a

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