QRdecompositionbased leastsquares lattice adaptive filter
adaptfilt.qrdlsl
will be removed in a future
release. Use dsp.AdaptiveLatticeFilter
instead.
ha = adaptfilt.qrdlsl(l,lambda,delta,coeffs,states)
ha = adaptfilt.qrdlsl(l,lambda,delta,coeffs,states)
returns
a QRdecompositionbased 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.qrdlsl
.
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 angle normalized backward prediction error states of the adaptive filter 
Since your adaptfilt.qrdlsl
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.qrdlsl
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  
 Returns the predicted samples generated during adaptation in the forward direction. See References — Adaptive Filters for details about linear prediction.  
 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. 
Implement Quadrature Phase Shift Keying (QPSK) adaptive equalization using a 32coefficient adaptive filter. To see the results of the equalization process in this example, look at the figure that follows the example code.
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)); % Baseband 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; % Softconstrained initialization factor ha = adaptfilt.qrdlsl(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'); axis('square'); xlabel('Real[y]'); ylabel('Imag[y]'); grid on;
Haykin, S.,Adaptive Filter Theory, 2nd Edition, Prentice Hall, N.J., 1991
adaptfilt.ftf
 adaptfilt.gal
 adaptfilt.lsl
 adaptfilt.qrdrls