lat2q.m

来自「This is mat lab code for adaptive lattic」· M 代码 · 共 57 行

function eo = lat2q(input,d,lambda,eta,order,B);

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


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


% Initialization of M-order vectors
gamma = ones(M+1,1);
beta = zeros(M,1);
delta = zeros(M,1);
rho = zeros(M,1);
kappaf = zeros(M,1);
kappab = zeros(M,1);
kappa = zeros(M,1);
   
xif = (1/eta*lambda*lambda)*ones(M,1);
xib = (1/eta*lambda*lambda)*ones(M,1);
for m=0:M-1
  xib(m+1) = xib(m+1)/lambda^m;
end
       
xif = quantize_v(xif,B);
xib = quantize_v(xib,B);

for i=1:N
  gammao = gamma;          
  betao = beta;
  xibo = xib;
  gamma = ones(M,1);
  beta(1) = input(i);
  alpha(1) = input(i);
  e(1) = d(i);
  for m=1:M
     xif(m) = quantize(quantize(lambda*xif(m),B) + quantize((abs(alpha(m)))^2*gammao(m),B),B);
     xib(m) = quantize(quantize(lambda*xib(m),B) + quantize((abs(beta(m)))^2*gamma(m),B),B);
                    
     delta(m) = quantize(quantize(lambda*delta(m),B) + quantize((alpha(m))'*(betao(m))*gammao(m),B),B);
     rho(m) = quantize(quantize(lambda*rho(m),B) + quantize((e(m))'*(beta(m))*gamma(m),B),B);
         
     beta(m+1) = quantize(betao(m) - quantize(kappab(m)*alpha(m),B),B);
     alpha(m+1) = quantize(alpha(m) - quantize(kappaf(m)*betao(m),B),B);
     e(m+1) = quantize(e(m) - quantize(kappa(m)*beta(m),B),B);

     gamma(m+1) = quantize(gamma(m) - quantize(abs(gamma(m)*beta(m))^2/xib(m),B),B);

     kappab(m) = quantize(delta(m)/xif(m),B);
     kappaf(m) = quantize((delta(m))'/xibo(m),B);
     kappa(m) = quantize((rho(m))'/xib(m),B);
   end
   eo(i) = e(M+1);
 end