mat_noise_tao.m

关于匹配滤波的相关计算

%chapter 2(5) add noise and delay tao0
clear;
%%%%%%%%%%%%%%%%%%%%%%%%1
t=1:0.01:100
T=50;
s1=rectpuls(t,T);
%%%%%%%%%%%%%%%%%%%%%%%%2
f0=0.5;
w0=2*pi*f0;
s2=s1.*sin(w0*t);
%%%%%%%%%%%%%%%%%%%%%%%%3
k=1;
s3=s1.*sin(w0*t+0.5*k*t.^2);
%%%%%%%%%%%%%%%%%%%%%%%%4
N=100;
tao=T/N;
s4=0;
for index=1:N
    rect=rectpuls(index*tao,T);
    c=round(rand)*2-1;
    s4=s4+c*rect.*sin(w0*t);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for s_num=1:4
figure(s_num)
switch s_num
    case 1
        signal=s1;
    case 2
        signal=s2;
    case 3
        signal=s3;
    case 4
        signal=s4;
end

tao0=1000;
signal_t=[zeros(1,tao0) signal];%delay tao0
[m,n]=size(signal_t);
noise = wgn(m,n,10); % white Gaussian noise
signal_noise=signal_t+noise;
%%%%%%%%%%%%%%%
subplot(221)
plot(signal_t)
title('signal with tao')
%%%%%%%%%%%%%%%
subplot(222)
plot(signal_noise)
title('add Gaussian noise')
%%%%%%%%%%%%%%%%%%%%%%%%%%
f1=fft(signal_noise);
f2=fft(fliplr(signal_noise));
sig_mat=ifft(f1.*f2);
subplot(223)
plot(sig_mat)
title('matched filter')
%%%%%%%%%%%%%%%%%%%
so=conv(signal_noise,sig_mat);
subplot(224)
plot(so)
title('output signal')
end