lpxdvvi.zip_线阵music算法

%classical high resolution DOA estimation algorithm %including Bartlett beamforming algorithm;Capon algorithm;Esprit; %MUSIC; WSF; etc; clear all; close all;clc; J=sqrt(-1); source_number=3; source_doa=[30 40 50]; sensor_number=8; snapshot_number=100; snr=10; A=exp(-J*(0:sensor_number-1)'*pi*sin(source_doa*pi/180)); s=(randn(source_number,snapshot_number)+J*randn(source_number,snapshot_number))/sqrt(2); x=A*s; y=awgn(x,snr); R=y*y'/snapshot_number; [V,D]=eig(R); Un=V(:,1:sensor_number-source_number); Gn=Un*Un'; searching_doa=0:0.1:90; for i=1:length(searching_doa) a_theta=exp(-J*(0:sensor_number-1)'*pi*sin(pi*searching_doa(i)/180)); P_BF(i)=abs((a_theta'*R*a_theta)./(a_theta'*a_theta)); P_capon(i)=1./abs((a_theta'*inv(R)*a_theta)); P_music(i)=1./abs((a_theta'*Gn*a_theta)); end figure(1); plot(searching_doa,P_BF); legend('Bartlett spectrum'); figure(2); plot(searching_doa,P_capon); legend('Capon spectrum'); figure(3); plot(searching_doa,P_music); legend('Music spectrum'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 程序2：以下是分布式目标参数估计算法 %Array signal processing %The new method to estimate DOA of Distributed signals %The proposed iteration method %2005.9.21 Guoxiansheng clc; clear all;close all; warning off MATLAB:divideByZero NN=100;%The number of snapshots NM=32;% The number of array units snm=2;%Signal numbers J=sqrt(-1);%Complex number unit ag=zeros(snm,1);%The DOA of the signals ag(1)=-2.5; ag(2)=-1.5;ag(3)=-2.5; delta1(1)=2;delta1(2)=10;delta1(3)=3; rho(1)=0.9;rho(2)=0.8;rho(3)=0.7; %delta(1)=0;delta(2)=0;delta(3)=0; agg=(ag/180)*pi; delta=(delta1/180)*pi; SNR=zeros(1,snm);%SNR of the snm signals for i=1:snm SNR(i)=10; end Pn=10^-4; %noise power An=sqrt(Pn/2); %noise amplitude Ps=10.^(SNR/10)*Pn;% signal power Amp=sqrt(Ps);%signal amplitude AA=zeros(NM,snm);%The array manifold AA(1,:)=1; noise=zeros(NM,1); for i=1:snm for j=2:NM %AA(j,i)=AA(j,i)+rho(i)^(j-1)*exp(J*pi*(j-1)*sin(agg(i))); %AA(j,i)=AA(j,i)+2*(1-cos(j-1)*delta(i))/((j-1)*delta(i)).^2*exp(J*pi*(j-1)*sin(agg(i))); AA(j,i)=AA(j,i)+exp(-(delta(i)*(j-1))^2/2)*exp(J*pi*(j-1)*sin(agg(i))); %AA(j,i)=AA(j,i)+sinc((j-1)*delta(i))*exp(J*pi*(j-1)*sin(agg(i))); end end fig=figure;plot(abs(fftshift(fft(AA))));zoom xon; sigs=zeros(snm,NN);%To generate the uncorrelated narrow band signals for i=1:snm for j=1:NN sigs(i,j)=Amp(i)*(randn+J*randn); end end yss=zeros(NM,NN);%The signals with noise for j=1:NN noise=An*(randn(NM,1)+J*randn(NM,1)); yss(:,j)=AA*sigs(:,j)+noise; end %%%%%%%%%%%%%%%%%%%%%%% %Traditional MUSIC Rx=zeros(NM,NM); for i=1:NN Rx=Rx+yss(:,i)*yss(:,i)'; end Rx=Rx/NN; [evector,evalue]=eig(Rx); evalue1=diag(evalue); Gn=evector(:,1:NM-snm); Us=evector(:,NM-snm+1:NM); Gnn=Gn*Gn'; NNN=160;qq=16; for i=1:NNN agv=(-(i-NNN)/180/qq)*pi; for j=1:NM Gs(j)=exp(J*pi*(j-1)*sin(agv)); end pp(i)=1/abs(Gs*Gnn*Gs'); end pp=pp/max(pp) fig=figure;plot(((1:NNN)-NNN)/qq,pp);zoom xon; title('traditional MUSIC'); %%%%%%%%%%%%%%%%% %Proposed for i=1:NNN agv=(-(i-NNN)/180/qq)*pi; for j=1:NM Gs(j)=(exp(J*pi*(j-1)*sin(agv))); end pp1=inv(real(diag(Gs)*Gnn*diag(Gs'))); [dde,ddv]=eig(pp1); %pp1=(diag(Gs)*((Gnn))*diag(Gs')); pp2=diag(ddv); for m=1:NM [vvi,vvv]=max(pp2); pp3(m,i)=log10(vvi); vv(vvv)=0; end end for m=1:snm pp3(m,:)=-(-1)^m*pp3(m,:)/max(pp3(m,:)); end %est_num1=detect_peak(pp3); %est_doa1=(est_num1-NNN)/qq; fig=figure; plot(((1:NNN)-NNN)/qq,pp3,'-d');zoom xon; legend('pp2(1)',2); % fig=figure; % plot(((1:NNN)-NNN)/qq,pp-pp3,'-d');zoom xon; title('Proposed MUSIC'); %%%%%%%%%%%%%%%%%%%%%%%%%%%% %maxmax for i=1:NNN agv=((i-NNN)/180/qq)*pi; for j=1:NM Gs(j)=(exp(J*pi*(j-1)*sin(agv)))^2; end qq1=Us'*diag(Gs)*(conj(Us)); [s,v]=eig(qq1*conj(qq1)); vv=(diag(v)); for m=1:snm [vvi,vvv]=max(vv); qq2(m,i)=log10(1/(1-vvi)); vv(vvv)=0; end end for m=1:snm qq2(m,:)=-(-1)^m*qq2(m,:);%/max(qq2(m,:)); end fig=figure; plot(((1:NNN)-NNN)/qq,qq2);zoom xon; fig=figure; plot(((1:NNN)-NNN)/qq,sum(qq2),'-d');zoom xon; disp('End of programme');