LMS.rar_NOISE_robust LMS

clear,clc m=8; % sensors n=2; % sources theta=[-20 0]; % in angle d=1/2; % 1/2 lambada N=500; % samples L=100; % resolution in [-90' 90'] Meann=0; % mean of noise varn=1; % variance of noise SNR=10; % signal-to-noise ratio INR=10; % interference-to-noise ratio rvar1=sqrt(varn) * 10^(SNR/20); % variance of signal rvar2=sqrt(varn) * 10^(INR/20); % variance of interference % generate the source signals s=[rvar1*exp(j*2*pi*50*0.001*[0:N-1]) rvar2*exp(j*2*pi*(100*0.001*[0:N-1]+rand))]; % generate the A matrix A=exp(-j*2*pi*d*[0:m-1].'*sin(theta*pi/180)); % generate the noise component e=sqrt(varn/2)*(randn(m,N)+j*randn(m,N)); % generate the ULA data Y=A*s+e; % initialize weight matrix and associated parameters for LMS predictor de =s(1, :); mu=1e-3; w = zeros(m, 1); for k = 1:N % predict next sample and error y(k) = w'*Y(:, k); e(k) = de(k) - y(k); % adapt weight matrix and step size w = w + mu * Y(:,k)*conj(e(k)); end % beamforming using the LMS method beam=zeros(1,L); for i = 1 : L a=exp(-j*2*pi*d*[0:m-1].'*sin(-pi/2 + pi*(i-1)/L)); beam(i)=20*log10(abs(w'*a)); end % plotting command followed figure angle=-90:180/L:(90-180/L); plot(angle,beam); xlabel('angle'); ylabel('幅度响应/dB'); figure for k = 1:N en(k)=(abs(e(k))).^2; end semilogy(en); xlabel('n'); ylabel('e^{2}(n)');