% Min-Norm AOA estimation for a M = 6 element array with noise variance = .1


M=6;
D = 2;  % number of signals
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
a=[1];
temp=zeros(1,M-1);
II=eye(M);
u1=II(:,1);
%u1=[1 temp];
for i=2:M
    a1=[a1 exp(-1j*i*pi*sin(th1))];
    a2=[a2 exp(-1j*i*pi*sin(th2))];
end
A=[a1.' a2.'];

Rss=[1 0;0 1];
Rrr=A*Rss*ctranspose(A)+sig2*eye(M);
[V,D]=eig(Rrr);
 N=V(:,1:4);      % calculate the noise subspace matrix of eigenvectors
for k=1:180;
   th(k)=-pi/6+pi*k/(3*180);
   clear a
a=[1];
   for jj=2:M
      a = [a exp(-1j*jj*pi*sin(th(k)))];
  end
  
P(k)=1./abs(conj(a)*N*N'*u1).^2; 

end
figure;
plot(th*180/pi,10*log10(P/max(P)),'k')
grid on
xlabel('Angle')
ylabel('|P(\theta)|')
axis([-30 30 -30 10])

%num2str(A,2)