tdoa.m

广义互相关算法和最小均方自适应滤波法主要用于两路接收信号的时间延迟估计

    function Jn_l=lms(miu,M,N,w0)
%read speech  it is a column vector;
r=wavread('D:\Program Files\MATLAB704\work\02.wav',300);
% cut the former 3000 points;

a=r(1:300,1)';x2=zeros(1,300);w=zeros(1,100);e=zeros(1,100);j=zeros(1,100);
x1=a;% r is the input noisy signal vector;
for n=1:290
    x2(n+10)=a(n);% D is the TDOA
end
x2=x2;% x1,x2 are the signals received

u=0.005; % step size
for n=1:100   %  select 1000 poiont of the data
    e(n)=x2(n+100)-w(n)*x1(n+100);
    w(n+1)=w(n)+u*e(n)*x1(n+100);
    j(n)=abs(e(n))^2;
end;
    figure;  plot(w,'r');
    figure; plot(j,'b'); 
 

%M=8;N=200;miu=0.005;w0=1*pi;

e_sum=zeros(1,N);
w_sum=zeros(M,N+1);

for l=1:100
    
    n=1:N;
    v=sqrt(0.1)*randn(1,N);
    un=exp(j*pi*n-j*pi)+exp(j*w0*n-j*0.79*pi)+v;

    u=[zeros(1,M) un];
    w=zeros(M,1);
    for i=1:N
        u_t=conj(fliplr(u(i:M+i-1)'));
        e(i)=u(M+i)-w(:,i)'*u_t;
        w(:,i+1)=w(:,i)+miu*u_t*e(i)';
    end
        
    w_sum=w_sum+w;
    e_sum=e_sum+abs(e).^2;
    
end

w_mean=w_sum./100;
e_mean=e_sum./100;
Jn_l=e_mean; 

plot(n,Jn_l)

a=-conj(w_mean(:,200));
wn=0:0.01:2*pi;
a=a';x=[1:M];
H=abs(1+a*exp(-j*x'*wn)).^2;
Pxx=1./H;
figure(2);plot(wn,abs(Pxx));