FIR adaptive filter that uses gradient lattice
adaptfilt.gal
has been removed. Use dsp.AdaptiveLatticeFilter
instead.
ha = adaptfilt.gal(l,step,leakage,offset,rstep,delta,
lambda,...rcoeffs,coeffs,states)
ha = adaptfilt.gal(l,step,leakage,offset,rstep,delta,
constructs a
gradient adaptive lattice FIR filter
lambda,...rcoeffs,coeffs,states)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.gal
.
Input Argument  Description 

 Length of the joint process filter coefficients. It must
be a positive integer and must be equal to the length of the reflection
coefficients plus one. 
 Joint process step size of the adaptive filter. This
scalar should be a value between zero and one. 
 Leakage factor of the adaptive filter. It must be a
scalar between 0 and 1. Setting leakage less than one implements
a leaky algorithm to estimate both the reflection and the joint
process coefficients. 
 Specifies an optional offset for the denominator of the
step size normalization term. It must be a scalar greater or equal
to zero. A nonzero 
 Reflection process step size of the adaptive filter.
This scalar should be a value between zero and one. 
 Initial common value of the forward and backward prediction
error powers. It should be a positive value. 0.1 is the default value
for 
 Specifies the averaging factor used to compute the exponentially
windowed forward and backward prediction error powers for the coefficient
updates. 
 Vector of initial reflection coefficients. It should
be a length (l1) vector. 
 Vector of initial joint process filter coefficients.
It must be a length 
 Vector of the backward prediction error states of the
adaptive filter. 
Since your adaptfilt.gal
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.gal
objects.
To show you the properties that apply, this table lists and describes
each property for the affine projection filter object.
Name  Range  Description 

 None  Defines the adaptive filter algorithm the object uses during adaptation 
 Specifies the averaging factor used to compute the exponentiallywindowed
forward and backward prediction error powers for the coefficient updates.
Same as the input argument  
 Returns the minimum meansquared prediction error. See References — Adaptive Filters for details about linear prediction.  
 Vector of elements  Vector containing the initial filter coefficients. It
must be a length 
 Any positive integer  Reports the length of the filter, the number of coefficients or taps 
 Returns the minimum meansquared prediction error in the forward direction. See References — Adaptive Filters for details about linear prediction.  
 0 to 1  Leakage parameter of the adaptive filter. If this parameter
is set to a value between zero and one, you implement a leaky GAL
algorithm. 
 Offset for the normalization terms in the coefficient
updates. Use this to avoid dividing by zero or by very small numbers
when input signal amplitude becomes 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 if you have not
changed the filter since you constructed it. 
 Coefficients determined for the reflection portion of the filter during adaptation.  
 Size of the steps used to determine the reflection coefficients.  
 Vector of elements  Vector of the adaptive filter states. 
 0 to 1  Specifies the step size taken between filter coefficient updates 
Perform a Quadrature Phase Shift Keying (QPSK) adaptive equalization using a 32coefficient adaptive filter over 1000 iterations.
D = 16; % Number of delay samples b = exp(1j*pi/4)*[0.7 1]; % Numerator coefficients a = [1 0.7]; % Denominator coefficients ntr= 1000; % 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) L = 32; % filter length mu = 0.007; % Step size ha = adaptfilt.gal(L,mu); [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;
To see the results, look at this figure.
Griffiths, L.J. "A Continuously Adaptive Filter Implemented as a Lattice Structure," Proc. IEEE^{®} Int. Conf. on Acoustics, Speech, and Signal Processing, Hartford, CT, pp. 683686, 1977
Haykin, S.,Adaptive Filter Theory, 3rd Ed., Upper Saddle River, NJ, Prentice Hall, 1996