www.pudn.com > aft.rar > run_qrd_rls_mvdr.m

function run_qrd_rls_mvdr(rp) 
 
% Computer Experiment 
% Section 14.5, Adaptive Filter Theory, 3rd edition 
% MVDR adaptive beamforming 
 
% modify path to suit 
% path(path, '/home/yee/aft/qrd_rls'); 
 
Ninit 	= rp.p; 
Ndata 	= Ninit + rp.Nsnaps; 
seed 	= 1; 
lambda = 1; 
 
% enter mean and variance of complex-valued AWGN 
rp.mean_v = 0; 
rp.var_v 	= 1; 
 
% A_1, phi_1 are target signal amplitude/elec. angle 
% A_2, phi_2 are interference signal amplitude/elec. angle 
% s is steering vector along elec. angle of look direction of interest 
A_1 = sqrt (rp.var_v) * 10^ (rp.TNRdB/20); 
phi_1 = pi*rp.sin_theta_1; 
A_2 = sqrt(rp.var_v) * 10^ (rp.INRdB/20); 
phi_2 = pi*rp.sin_theta_2; 
s = exp(-j*[0:(rp.p-1)]'*phi_1); 
 
% setup input/output sequences 
 
for i = 1:Ndata, 
	% setup random disturbances 
	randn('seed', i); 
	vr 	= sqrt(rp.var_v/2) * randn(1, rp.p) + rp.mean_v; 
	vi 	= sqrt(rp.var_v/2) * randn(1, rp.p) + rp.mean_v; 
	v 	= vr + j*vi; 
	rand('seed', i); 
	Psi = 2*pi*rand(1); 
	Xi(i, :) = A_1*exp(j*[1:rp.p]*phi_1) + A_2*exp(j*[1:rp.p]*phi_2 + Psi) + v; 
end; 
 
Y = zeros(1, Ndata); 
 
% run bea/nformer for indicated number of snapshots 
[Wo, xp, gamma, e] = qrd_rls_AR_pred(Xi, Y, rp.verbose, lambda, 'mvdr', s); 
 
% test vectors for spatially sampled response 
W_H = Wo(Ndata, :); 
st = -1:0.025:1; 
est = exp(j*pi*[0:(rp.p-1)]'*st); 
 
W=Wo; % simple renaming so that the format jives with thtat expected by the 
	   % plot_mvdr.m routine 
 
eval(['save ' rp.name])