www.pudn.com > adaptivefiltering.rar > run_lms_mvdr.m

function run_lms_mvdr(rp) 
 
% Computer Experiment 
% Section 9.8, Adaptive Filter Theory, 3rd edition 
% MVDR adaptive beamforming using the LMS algorithm 
 
Ninit   = rp.p; 
Ndata   = Ninit + rp.Nsnaps; 
seed    = 1; 
 
% A_i, phi_l 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); 
e = s(2:rp.p); 
 
% 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; 
 
% setup effective desired output and input vectors from 
% original data 
g = 1; 
 
d = g * Xi(:, 1); 
u = diag(Xi(:, 1)) * (ones(Ndata, 1) * e.') - Xi(:, 2:rp.p); 
    
[W, xp] = lms(u, d, rp.mu, rp.decay, rp.verbose); 
Wo      = g - W * conj(e); 
W       = [Wo W]; 
 
eval(['save ' rp.name])