aepmethod4.m

空时自适应信号处理

%降维STAP方法的两种基本形式的比较研究(最小特征值对应的特征向量构成变化矩阵T)
clc;clear all;close all;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
N= 8;                      % number of sensors
M= 8;                      % number of pulse
lamda=0.03;               % wavelength
V_p=90;                    % platform velocity
d=0.015;                   % spacing of sensors
PRF=12000;                 % pulse repetition frequency
T=1/PRF;                   % pulse repetition interval
H=500;                     % platform height    
R=1000;                    % range
theta=asin(H/R);           % depression angle
Bc=0.05;                      % clutter bandwidth
mm=0:1:N-1;mm=mm';
nn=0:1:M-1;nn=nn';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%V_d=20;
%fai_darget=pi/2;
%Wd_d=4*pi*V_d*cos(fai_darget)/(lamda*PRF);         %目标多普勒角频率
%Ws_d=2*pi*d*cos(fai_darget)/lamda;                 %目标空间角频率
%bM_d=exp(j*Wd_d*mm);                                %M×1目标维时间导向矢量
%aN_d=exp(j*Ws_d*nn);                                %N×1目标维空间导向矢量
%S_d=kron(bM_d,aN_d); 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% %Clutter Covariance Matrix% %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for m=1:1:M
    for p=1:1:M
        for i=1:1:N
            for k=1:1:N
                l=(m-1)*N+i;
                n=(p-1)*N+k;
                phi=linspace(0,2*pi,61);
                D=0.5*(1+cos(2*(phi-pi/2)));
                D=D.^2;                
                                
                %D=1;% 有无加权影响很大！
                G=1;                    
                t_phase=exp(j*2*pi/lamda*2*V_p*(m-p)*T*cos(phi));
                s_phase=exp(j*2*pi/lamda*(i-k)      *d*cos(phi));
                integral=D.*t_phase.*s_phase.*G;
                
                Q(l,n)=sum(integral)/60;
                %Q(l,n)=Q(l,n)*exp(-Bc*Bc*(m-p)^2/8);                %杂波带宽
                
            end
        end
    end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Q=Q+0.001*eye(N*M,N*M);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%一个干扰
phi=4*pi/6;
for m=1:1:M
    p=m;
    for i=1:1:N
        for k=1:1:N
            l=(m-1)*N+i;
            n=(p-1)*N+k;             
            Q1(l,n)=0.25*exp(j*2*pi/lamda*(i-k)*d*cos(phi));            
        end
    end
end
%Q=Q+Q1;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fd_d=0.5;
AA=exp(j*2*pi*nn);
BB=exp(j*2*pi*mm*fd_d);
S_d=kron(AA,BB);         %search channel
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%特征分解AEP
Q_tr=trace(Q);           %
Q_inv=inv(Q);            %
Q_d=eig(Q);
Q_d=flipud(Q_d);
[V,D]=eig(Q);            %对杂波协方差矩阵进行特征分解

%最小特征值对应的特征向量构成变化矩阵T
%TT=V(:,1:50);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%SINR metric method
for i=1:M*N
    SINRM(1,i)=(sum(S_d.*V(:,i)))^2/Q_d(i,1);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[YS,IS]=sort(SINRM);
YS=fliplr(YS);
IS=fliplr(IS);
r=50;
%for i1=1:r
   %TT(:,i1)=V(:,IS(i1));
   %end
%QT=TT'*Q*TT;
%QT_inv=inv(QT);
%% IF %%
fd=0.5;
for ii=1:M*N
    
    AAi=exp(j*2*pi*nn);
    BBi=exp(j*2*pi*mm*fd);
    S=kron(BBi,AAi);
    %IFopt(ii)=S'*Q_inv*S;
    TT=V(:,1:ii);
    QT=TT'*Q*TT;
    QT_inv=inv(QT);
    ST=TT'*S;
    WT=QT_inv*ST;
    IFaep(ii)=ST'*WT;
end
IFaep=20*log10(IFaep/max(IFaep));    
    
%figure(1);
%fd=-0.5:0.01:0.5;
%plot(fd,IFopt,'-b');
%hold on;
%plot(fd,IFaep,'r');
%xlabel('归一化多普勒频率');ylabel('IF dB');
%axis([-0.5,0.5,-65,0]);

%figure(2);                   %信噪比尺度图
%i=1:M*N;
%plot(i,abs(SINRM)/abs(max(SINRM)));
%hold on;
%plot(i,Q_d/max(Q_d),'r');
%xlabel('number');

figure(3);
ii=1:N*M
plot(ii,IFaep,'r');
xlabel('number');ylabel('SINR dB');