tracking_doa.m

用于智能天线中自适应的移动目标的跟踪

function [Count_signals, sorted_matrix,sorted_value]=music_doa(No_signals,Angle_arrivals,...
              Freq_signal,No_elements,Element_spacing,lambda,No_samples)
          
Kappa=2*pi/lambda;
d=Element_spacing;

%generating signals:
Total_signal=zeros(No_signals,No_samples);
k=1:No_samples;
for i=1:No_signals
    theta=180*(2*rand-1);
    theta=theta*pi/180;
    Total_signal(i,:)=sqrt(2)*sin(2*Freq_signal(i)*pi*k); %+theta:frequency offset
    Total_signal(i,:)=randBW(No_samples);
end

%generating noise:add to the signal at each of the rx. element:
noise=zeros(No_signals,No_samples);
SNR=20;
noise_var=1/(10^(SNR/10));
for i=1:No_elements
    noise(i,:)=sqrt(noise_var)*randn(1,No_samples);
end

%Generating the array propagation vector(steering vector) for each signal:
Prop_matrix=zeros(No_elements,No_signals);
k=0:No_elements-1;
for i=1:No_signals
    temp=exp(-j.*k*Kappa*d*sin(Angle_arrivals(i)));
    Prop_matrix(:,i)=temp.';
end

% waveform received at the elements:
U=zeros(No_elements,No_samples);
for i=1:No_samples
    U(:,i)= Prop_matrix*Total_signals(:,i)+noise(:,i);
end

%Estimate the input covariance matrix:
Cov_matrix=zeros(No_elements,No_elements);
for i=1:No_samples
    temp=U(:,i)*(U(:,i)');
    Cov_matrix=Cov_matrix+temp;
end
Cov_matrix=Cov_matrix/No_samples;

%find the Eigen values and the corresponding eign vectors:
[Eig_matrix,Eig_value]=eig(Cov_matrix,'nobalance');

%Estimate the number of incident signals:
[sorted_matrix,sorted_value,Count_signals,V_matrix]=Eigsort(Eig_matrix,...
    Eig_value,No_elements,noise_var);

figure(1)
plot([1:elements],abs(fliplr(sorted_value)),'-+');
grid on 
hold on
plot([1:elements],noise_var*[ones(1,No_elements)],'-o');

legend('Eigen values', 'Array elements');
xlabel('index');
ylabel('Eigen value');
title('Magnitude plot of Eigen values of the correlation matrix of rxd. signal');

%plot the spectrum:
plotspectrum(V_matrix,No_elements,Kappa,d);