Least squares lattice adaptive filter
adaptfilt.lsl
will be removed in a future
release. Use dsp.AdaptiveLatticeFilter
instead.
ha = adaptfilt.lsl(l,lambda,delta,coeffs,states)
ha = adaptfilt.lsl(l,lambda,delta,coeffs,states)
constructs
a least squares lattice adaptive filter 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.lsl
.
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 prediction coefficients plus one. L defaults to 10. 
 Forgetting factor of the adaptive filter. This is a scalar
and should lie in the range (0, 1]. 
 Softconstrained initialization factor in the least squares
lattice algorithm. It should be positive. 
 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.lsl
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.lsl
objects.
To show you the properties that apply, this table lists and describes
each property for the filter object.
Name  Range  Description 

 None  Defines the adaptive filter algorithm the object uses during adaptation. 
 Returns the predicted samples generated during adaptation. 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. 
 Forgetting factor of the adaptive filter. This is a scalar
and should lie in the range (0, 1]. It defaults to 1. Setting  
 Contains the predicted values for samples during adaptation. Compare these to the actual samples to get the error and power.  
 Softconstrained initialization factor. This scalar should
be positive and sufficiently large to prevent an excessive number
of Kalman gain rescues.  

 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. 
 Vector of elements, data type double  Vector of the adaptive filter states. 
Demonstrate Quadrature Phase Shift Keying (QPSK) adaptive equalization using a 32coefficient adaptive filter running for 1000 iterations. After you review the example code, the figure shows the results of running the example to use QPSK adaptive equalization with a 32ndorder FIR filter. The error between the inphase and quadrature components, as shown by the errors plotted in the upper plots, falls to near zero. Also, the equalized signal shows the clear quadrature nature.
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= 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) lam = 0.995; % Forgetting factor del = 1; % initialization factor ha = adaptfilt.lsl(32,lam,del); [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'); grid on; axis('square'); xlabel('Real[y]'); ylabel('Imag[y]');
Haykin, S., Adaptive Filter Theory, 2nd Edition, Prentice Hall, N.J., 1991
adaptfilt.ftf
 adaptfilt.gal
 adaptfilt.qrdlsl
 adaptfilt.rls