testlmslattice.m

来自「卡尔曼滤波器设计的一个例子」· M 代码 · 共 43 行

% LMSLATTICE used in a simple system identification application.
% By the end of this script the adaptive filter w 
% should have the same coefficients as the unknown filter h.

clear all;
iter = 5000;                  % Number of samples to process
% Complex unknown impulse response
h    = [.9 + i*.4; 0.7+ i*.2; .5; .3+i*.1; .1];     
 
xn   = 2*(rand(iter,1)-0.5);  % Input signal, zero mean random.

% although xn is real, dn will be complex since h is complex
dn   = filter(h,1,xn);        % Unknown filter output 
en   = zeros(iter,1);         % vector to collect the error

% Initialize LMSLATTICE with a filter of 10 coef.
L		= 10;					% filter length
mu_c 	= .01;				% linear combiner step size
mu_p 	= 0.001;				% linear predictor step size
uk = 1;
[k,w,b,P,d,y,e] 	= init_lmslattice(L);% Init LMS Lattice algorithm 

%% Processing Loop
for (m=1:iter)
   if (m == 2000), uk=0;end  % stop updating k after 1000 samples
   x = xn(m,:);  % new input sample
   d = dn(m,:) + 1e-3*rand;      % additive noise of var = 1e-6 
   [k,w,b,P,y,e]=asptlmslattice(k,w,b,P,x,d,mu_p,mu_c,uk);
   
   % save the last error sample to plot later
   en(m,:) = e;  
end;

% display the results
subplot(2,2,1);stem([real(w) imag(conj(w))]); grid;
xlabel('filter after convergence')
subplot(2,2,2);
eb = filter(1, [1 -.9], en .* conj(en));
plot(10*log10(eb  ));grid
axis([0 5000 -80 0]);
ylabel('estimation error [dB]')
xlabel('Learning curve')