Luca_Romano
Junior Member level 2
Hello all,
I'm using the following 1D music algorithm to estimate targets position (only theta angle).
The algorithm is a slightly modified version of the music algorithm posted by the user weetabixharry in this forum (https://www.edaboard.com/showthread.php?t=79938&highlight=steering+music).
It works pretty good with my 1D antenna array. I need help to trasform it in the 2D case (azimuth and range) in near field scenario.
Thanks a lot in advance,
Ciao
Luca
I'm using the following 1D music algorithm to estimate targets position (only theta angle).
The algorithm is a slightly modified version of the music algorithm posted by the user weetabixharry in this forum (https://www.edaboard.com/showthread.php?t=79938&highlight=steering+music).
It works pretty good with my 1D antenna array. I need help to trasform it in the 2D case (azimuth and range) in near field scenario.
Thanks a lot in advance,
Ciao
Luca
Code:
% ======= (1) TRANSMITTED SIGNALS ======= %
% Signal source directions
az = [35;39;127]; % Azimuths
el = zeros(size(az)); % Simple example: assume elevations zero
M = length(az); % Number of sources
% Transmitted signals
L = 200; % Number of data snapshots recorded by receiver
m = randn(M,L); % Example: normally distributed random signals
% ========= (2) RECEIVED SIGNAL ========= %
% Wavenumber vectors (in units of wavelength/2)
k = pi*[cosd(az).*cosd(el), sind(az).*cosd(el), sind(el)].';
% Array geometry [rx,ry,rz]
N = 10; % Number of antennas
r = [(-(N-1)/2:(N-1)/2).',zeros(N,2)]; % Assume uniform linear array
% Matrix of array response vectors
A = exp(-1j*r*k);
% Additive noise
sigma2 = 0.01; % Noise variance
n = sqrt(sigma2)*(randn(N,L) + 1j*randn(N,L))/sqrt(2);
% Received signal
x = A*m + n;
% ========= (3) MUSIC ALGORITHM ========= %
% Sample covariance matrix
Rxx = x*x'/L;
d = 0.5;% Distance between elements in Wavelenght
[Q ,D]=eig(Rxx); %Compute eigendecomposition of covariance matrix
[D,I]=sort(diag(D),1,'descend'); %Find r largest eigenvalues
Q=Q (:,I); %Sort the eigenvectors to put signal eigenvectors first
Qs=Q (:,1:M); %Get the signal eigenvectors
Qn=Q(:,M+1:N); %Get the noise eigenvectors
% MUSIC algorithm
% Define angles at which MUSIC “spectrum” will be computed
angles=(-90:1:90);
for k=1:length(angles)
a1(:,k)=exp(-1i*2*pi*(d*(0:N-1)'*sin([angles(k).']*pi/180)));
end
for k=1:length(angles)
%Compute MUSIC “spectrum”
music_spectrum(k)=(a1(:,k)'*a1(:,k))/(a1(:,k)'*(Qn*Qn')*a1(:,k));
end
plot(angles,abs(music_spectrum))
grid on
title('MUSIC Spectrum')
xlabel('Angle in degrees')