doaestimate1.m

通信信号处理中的七阵元四信号元DOA估计

lambda	= 2;			% wavelength.
D 	= 0.5*lambda;		% Element separation.
T 	= 2000;			% Number of snapshots.
K 	= 7;			% Number of digital antenna channels.

theta	= d2r([45 60 30 135])';	% Target angles. The number of targets is
				%  given by the number of target angles.
phi	= zeros(size(theta));	% Target angles.
SNR	= [10 10 10 10]';		% Signal to noise ratio in dB at each
				%  antenna element!?!
alpha	= d2r([0 0 0 0])';		% Start phases of the target signals.
dalpha	= d2r([5.4 18 7.2 12.6])';	% A constant phase shift between snapshots.
				%  Means targets movements at constant velocity.
dist=Inf*ones(size(theta));	% Distances to the sources.			
tgtModel      	= 'const';	% Target type to simulate.
noiseModel     	= 'rndnw';	% Noise type to simulate.
%noiseModel     	= 'rndn';	% Noise type to simulate.
MMu	= 4;			% Number of targets that MUSIC believes in.


% ----------------------------------------------------------------------- %
% Commands.
% ----------------------------------------------------------------------- %

ant = defant('isotropULA',[K,D]);		% Define the antenna.
ww=[theta, phi, SNR, alpha, dalpha, dist, eye(size(theta,1))];  
sig = compsim4(ant, lambda, T, tgtModel,ww, noiseModel, eye(K));	


spect1 = sdoaspc('music',sig,[],MMu);		% Estimate the DOA-spectrum
						%  with MUSIC.

R = ecorrm(sig);				% Estimate the antenna signals
						%  correlation matrix.

spect2 = sdoaspc('cbf',R);			% Estimate the DOA-spectrum
						%  with conventional beamform.
spect3 = sdoaspc('eigvec',R);			% Estimate the DOA-spectrum
spect4 = sdoaspc('pisa',R);			% Estimate the DOA-spectrum

figure,splot2(spect1,'',spect2,'',spect3,'',spect4) % Plot all DOA-spectrums.
legend('Music','Conventional beamforming','Eigenvector method','Pisarenko')

%printfm dbtex1a.eps [] 12