lat6q.m

This is mat lab code for adaptive lattice filters.

function eo = lat6q(input,d,lambda,eta,order,B);

% Quantized QR-array 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);
b = zeros(M,1);
qf = zeros(M,1);
qb = zeros(M,1);
q = 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 = sqrt(xif);
xib = sqrt(xib);
xif = quantize_v(xif,B);
xib = quantize_v(xib,B);

xibo = xib;
   
for i=1:N
  bo = b;
  gamma = ones(M+1,1); % square-root of gamma denoted by gamma still
  b(1) = input(i);
  f(1) = input(i);
  r(1) = d(i);
     
  for m=1:M
     A1 = [quantize(lambda^(1/2)*xibo(m),B) bo(m); quantize(lambda^(1/2)*(qf(m))',B) (f(m))'];
     [c s] = givens(A1(1,1),A1(1,2));
     c = quantize(c,B);
     s = quantize(s,B);
     B1 = A1*[c -s; conj(s) c];
     qf(m) = quantize((B1(2,1))',B);
     f(m+1) = quantize((B1(2,2))',B);
     xib(m) = quantize(B1(1,1),B);
     xibo(m) = xib(m);
         
     A2 = [quantize(lambda^(1/2)*xib(m),B) b(m); quantize(lambda^(1/2)*(q(m))',B) (r(m))'; 0 gamma(m)];
     [c s] = givens(A2(1,1),A2(1,2));
     c = quantize(c,B);
     s = quantize(s,B);
     
     B2 = A2*[c -s; conj(s) c];
     xib(m) = quantize(B2(1,1),B);
     q(m) = quantize((B2(2,1))',B);
     r(m+1) = quantize((B2(2,2))',B);
     gamma(m+1) = quantize(B2(3,2),B); % square-root of conversion factor
          
         
     A3 = [quantize(lambda^(1/2)*xif(m),B) f(m); quantize(lambda^(1/2)*(qb(m))',B) (bo(m))'];
     [c s] = givens(A3(1,1),A3(1,2));
     c = quantize(c,B);
     s = quantize(s,B);
     B3 = A3*[c -s; conj(s) c];
     xif(m) = quantize(B3(1,1),B);
     qb(m) = quantize((B3(2,1))',B);
     b(m+1) = quantize((B3(2,2))',B);
         
     ro(m+1) = quantize(r(m+1)*gamma(m+1),B);
  end
  eo(i) = quantize(r(M+1)/gamma(M+1),B);
end