music.m

陈列信号处理中MUSIC算法的仿真及性能分析

%
%完整的MUSIC算法
clear
Sensor=8; % Number of array elements
Snap=200; % Number of snapshots
Doa=[15 -15 0];%*pi/180; % Sources DOA's in radian
S_number=length(Doa); % Number of sources
Lambda=0.1; % Wavelength of Sources
d=0.5*Lambda; % Sensor spacings
S_x=[0:Sensor-1]*d; % Sensor position for ULA
A=exp(-j*2*pi/Lambda*S_x'*sin(Doa*pi/180));
Snr=10; % Signal-to-noise ratio in dB
Snr=sqrt(10.^(Snr/10));
Sig=sqrt(0.5)*(randn(S_number,Snap)+j*randn(S_number,Snap));
% Signal waveform (unit power)
Noise=sqrt(0.5)*(randn(Sensor,Snap)+j*randn(Sensor,Snap));
% Additive noise (unit power)
X=Snr*A*Sig+Noise; % Complete data generation
P=zeros(Sensor,Sensor);
for i=1:Snap
    P=P+X(:,i)*X(:,i)';
end
   P=P/Snap;%Snap; % Array covariance matrix estimation
[U V D]=svd(P);
En=U(:,S_number+1:Sensor); % Estimation of noise subspace
theta=[-90:0.1:90]*pi/180;
for num=1:length(theta)
A_search=exp(-j*2*pi/Lambda*S_x'*sin(theta(num)));
P_music(num)=abs(1/(A_search'*En*En'*A_search));
end
h=[-90:0.1:90];
s1=10*log10(P_music/max(P_music));
plot(h,s1);
axis([-90 90 -90 10]);
xlabel('方位角(度)');
ylabel('空间谱(dB)');
%legend('4 array elements','8 array elements','16 array elements',2);
grid;
%for t=850:950
    %y1(t-849)=P_music(t);
%end
%[y1_max,I1_max]=max(y1);
%I1_max=(I1_max+849-1)/10-90
%for t=700:800
 %   y2(t-699)=P_music(t);
%end
 %   [y2_max,I2_max]=max(y2) ; 
  %  I2_max=(I2_max+699-1)/10-90
%for t=1000:1100
 %   y3(t-999)=P_music(t);
%end
%[y3_max,I3_max]=max(y3);
%I3_max=(I3_max+999-1)/10-90