Chap_05_MVDR.zip_mvdr _自适应mvdr

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])