FIR adaptive filter that uses frequencydomain with bin step size normalization
adaptfilt.fdaf
will be removed in a future
release. Use dsp.FrequencyDomainAdaptiveFilter
instead.
ha = adaptfilt.fdaf(l,step,leakage,delta,lambda,blocklen,
offset,...coeffs,states)
ha = adaptfilt.fdaf(l,step,leakage,delta,lambda,blocklen,
constructs a frequencydomain
FIR adaptive filter
offset,...coeffs,states)ha
with bin step size normalization.
If you omit all the input arguments you create a default object with l = 10 and step
=
1.
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.fdaf
.
Input Argument  Description 

 Adaptive filter length (the number of coefficients or taps). l must be a positive integer; it defaults to 10 when you omit the argument. 
 Step size of the adaptive filter. This is a scalar and
should lie in the range (0,1]. 
 Leakage parameter of the adaptive filter. If this parameter
is set to a value between zero and one, you implement a leaky FDAF
algorithm. 
 Initial common value of all of the FFT input signal powers.
Its initial value should be positive. 
 Specifies the averaging factor used to compute the exponentiallywindowed
FFT input signal powers for the coefficient updates. 
 Block length for the coefficient updates. This must be
a positive integer. For faster execution, ( 
 Offset for the normalization terms in the coefficient
updates. Use this to avoid divide by zeros or by very small numbers
when any of the FFT input signal powers become very small. 
 Initial timedomain coefficients of the adaptive filter. 
 The adaptive filter states. 
Since your adaptfilt.fdaf
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.fdaf
objects.
To show you the properties that apply, this table lists and describes
each property for the adaptfilt.fdaf
filter object.
Name  Range  Description 

 None  Defines the adaptive filter algorithm the object uses during adaptation. 
 (0, 1]  Specifies the averaging factor used to compute the exponentiallywindowed
FFT input signal powers for the coefficient updates. Same as the input
argument 
 Any integer  Block length for the coefficient updates. This must be
a positive integer. For faster execution, ( 
 Stores the discrete Fourier transform of the filter coefficients
in  
 States for the FFT operation.  
 Any positive integer  Reports the length of the filter, the number of coefficients or taps. 
 Leakage parameter of the adaptive filter. if this parameter
is set to a value between zero and one, you implement a leaky FDAF
algorithm.  
 Any positive real value  Offset for the normalization terms in the coefficient
updates. Use this to avoid dividing by zero or by very small numbers
when any of the FFT input signal powers become very small. 

 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. 
 A vector of 2*  
 Any scalar from zero to one, inclusive  Specifies the step size taken between filter coefficient updates 
Quadrature Phase Shift Keying (QPSK) adaptive equalization using 1024 iterations of a 32coefficient FIR filter. After this example code, a figure demonstrates the equalization results.
D = 16; % Number of samples of delay b = exp(1j*pi/4)*[0.7 1]; % Numerator coefficients of channel a = [1 0.7]; % Denominator coefficients of channel ntr= 1024; % Number of iterations s = sign(randn(1,ntr+D))+1j*sign(randn(1,ntr+D)); %QPSK signal n = 0.1*(randn(1,ntr+D) + 1j*randn(1,ntr+D)); % Noise signal r = filter(b,a,s)+n; % Received signal x = r(1+D:ntr+D); % Input signal (received signal) d = s(1:ntr); % Desired signal (delayed QPSK signal) del = 1; % Initial FFT input powers mu = 0.1; % Step size lam = 0.9; % Averaging factor ha = adaptfilt.fdaf(32,mu,1,del,lam); [y,e] = filter(ha,x,d); subplot(2,2,1); plot(1:ntr,real([d;y;e])); title('InPhase Components'); legend('Desired','Output','Error'); xlabel('Time Index'); ylabel('signal value'); subplot(2,2,2); plot(1:ntr,imag([d;y;e])); title('Quadrature Components'); legend('Desired','Output','Error'); xlabel('Time Index'); ylabel('signal value'); subplot(2,2,3); plot(x(ntr100:ntr),'.'); axis([3 3 3 3]); title('Received Signal Scatter Plot'); axis('square'); xlabel('Real[x]'); ylabel('Imag[x]'); grid on; subplot(2,2,4); plot(y(ntr100:ntr),'.'); axis([3 3 3 3]); title('Equalized Signal Scatter Plot'); axis('square'); xlabel('Real[y]'); ylabel('Imag[y]'); grid on;
Shynk, J.J.,"FrequencyDomain and Multirate Adaptive Filtering," IEEE^{®} Signal Processing Magazine, vol. 9, no. 1, pp. 1437, Jan. 1992
adaptfilt.blms
 adaptfilt.blmsfft
 adaptfilt.pbfdaf
 adaptfilt.ufdaf