MATLAB+信号处理超级学习手册-程序代码

function eo = lat1q(input,d,lambda,eta,order,B); % Quantized a posteriori-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); delta = zeros(M,1); rho = 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; bo = b; xibo = xib; gamma = ones(M,1); b(1) = input(i); f(1) = input(i); r(1) = d(i); for m=1:M xif(m) = quantize(quantize(lambda*xif(m),B) + quantize((abs(f(m)))^2/gammao(m),B),B); xib(m) = quantize(quantize(lambda*xib(m),B) + quantize((abs(b(m)))^2/gamma(m),B),B); delta(m) = quantize(quantize(lambda*delta(m),B) + quantize((f(m))'*(bo(m))/gammao(m),B),B); rho(m) = quantize(quantize(lambda*rho(m),B) + quantize((r(m))'*(b(m))/gamma(m),B),B); gamma(m+1) = quantize(quantize(gamma(m) - quantize(abs(b(m))^2/xib(m),B),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); b(m+1) = quantize(bo(m) - quantize(kappab(m)*f(m),B),B); f(m+1) = quantize(f(m) - quantize(kappaf(m)*bo(m),B),B); r(m+1) = quantize(r(m) - quantize(kappa(m)*b(m),B),B); end eo(i) = quantize(r(M+1)/gamma(M+1),B); end 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 function eo = lat3q(input,d,lambda,eta,order,B); % Quantized a priori-based error feedback 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); beta = 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+1,1); 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); 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); kappa(m) = quantize(kappa(m) + quantize((beta(m))'*gamma(m)*e(m+1)/xib(m),B),B); kappaf(m) = quantize(kappaf(m) + quantize((betao(m))'*gammao(m)*alpha(m+1)/xibo(m),B),B); kappab(m) = quantize(kappab(m) + quantize((alpha(m))'*gammao(m)*beta(m+1)/xif(m),B),B); gamma(m+1) = quantize(gamma(m) - quantize(abs(gamma(m)*beta(m))^2/xib(m),B),B); end eo(i) = e(M+1); end function eo = lat4q(input,d,lambda,eta,order,B); % Quantized a posteriori-based error feedback 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); 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); xibo = xib; for i=1:N gammao = gamma; bo = b; xiboo = xibo; xibo = xib; xifo = xif; gamma = ones(M+1,1); b(1) = input(i); f(1) = input(i); r(1) = d(i); for m=1:M xif(m) = quantize(quantize(lambda*xif(m),B) + quantize((abs(f(m)))^2/gammao(m),B),B); xib(m) = quantize(quantize(lambda*xib(m),B) + quantize((abs(b(m)))^2/gamma(m),B),B); gamma(m+1) = quantize(gamma(m) - quantize(abs(b(m))^2/xib(m),B),B); a1 = quantize(kappa(m) + quantize(((b(m))'*r(m))/(lambda*gamma(m)*xibo(m)),B),B); kappa(m) = quantize((gamma(m+1)/gamma(m))*a1,B); a1 = quantize(kappaf(m) + quantize(((bo(m))'*f(m))/(lambda*gammao(m)*xiboo(m)),B),B); kappaf(m) = quantize((gammao(m+1)/gammao(m))*a1,B); a1 = quantize(kappab(m) + quantize(((f(m))'*bo(m))/(lambda*gammao(m)*xifo(m)),B),B); kappab(m) = quantize((gamma(m+1)/gammao(m))*a1,B); b(m+1) = quantize(bo(m) - quantize(kappab(m)*f(m),B),B); f(m+1) = quantize(f(m) - quantize(kappaf(m)*bo(m),B),B); r(m+1) = quantize(r(m) - quantize(kappa(m)*b(m),B),B); end eo(i) = quantize(r(M+1)/gamma(M+1),B); end 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