lat5q.m

This is mat lab code for adaptive lattice filters.

function eo = lat5q(input,d,lambda,eta,order,B);

% Quantized normalized RLS lattice filter
% returns the a-priori output error sequence


N = max(size(input));
M = order;


% Initialization of M-order vectors
 b = zeros(M,1);
 kappaa = zeros(M,1);
 kappac = zeros(M,1);
 xi = (1/eta*lambda*lambda);  % scalar xi_0(-1)
 xib = (1/eta*lambda*lambda); % scalar xi_0^b(-1)
 pb = ones(M,1);

 xi = quantize(xi,B);
 xib = quantize(xib,B);
 
 
 for i=1:N
   bo = b;
   pbo = pb;
   gamma = ones(M,1); % added only to obtain the a priori error
   xib = quantize(quantize(lambda*xib,B) + quantize(abs(input(i))^2,B),B);
   xi = quantize(quantize(lambda*xi,B) + quantize(abs(d(i))^2,B),B);
   b(1) = quantize(input(i)/sqrt(xib),B);
   f(1) = b(1);
   r(1) = quantize(d(i)/sqrt(xi),B);
   sigma(1) = quantize(sqrt(xi),B);
   for m=1:M
     pb(m) = quantize(sqrt(1-abs(b(m))^2),B);
     pf(m) = quantize(sqrt(1-abs(f(m))^2),B);
     p(m)  = quantize(sqrt(1-abs(r(m))^2),B);

     kappaa(m) = quantize(quantize(kappaa(m)*pbo(m)*pf(m),B) + quantize(f(m)*(bo(m))',B),B);
     kappac(m) = quantize(quantize(kappac(m)*pb(m)*p(m),B) + quantize((b(m))'*r(m),B),B);

     pa(m) = quantize(sqrt(1-abs(kappaa(m))^2),B);
     pc(m) = quantize(sqrt(1-abs(kappac(m))^2),B);
        
     r(m+1) = quantize((1/(pb(m)*pc(m))*quantize(r(m)-quantize(kappac(m)*b(m),B),B)),B);
     b(m+1) = quantize((1/(pf(m)*pa(m))*quantize(bo(m)-quantize((kappaa(m))'*f(m),B),B)),B);
     f(m+1) = quantize((1/(pbo(m)*pa(m))*quantize(f(m)-quantize(kappaa(m)*bo(m),B),B)),B);

     sigma(m+1) = quantize(sigma(m)*pc(m)*pb(m),B);
     ro(m+1) = quantize(sigma(m+1)*r(m+1),B); % a posteriori error
        
     gamma(m+1) = quantize(gamma(m)*pb(m)*pb(m),B);  % necessary only to recover a priori error
   end
   eo(i) = quantize(ro(M+1)/gamma(M+1),B); % a priori error
end