matlab编程.rar_SAR图像数据_img matlab_img格式 matlab_perhapsj3k_读取sar

a1=-0.1; a2=0.8; r0=1; r1=a1*r0/(1-a2); r2=a1*r1+a2*r0; %% %LMS a1=0.1; a2=-0.8; delta=0.05; N=500; %生成白噪声，并生成x(n)，实现100次 for i=1:1:100 whi=sqrt(0.27)*randn(1,N); v(i,1)=whi(1); x(i,1)=whi(1); v(i,2)=whi(2); x(i,2)=-a1*x(1)+whi(2); for j=3:1:N v(i,j)=whi(j); x(i,j)=-a1*x(i,j-1)-a2*x(i,j-2)+whi(j); end end %LMS算法计算a1、a2 h=zeros(100,N,2); for i=1:1:100 h10=0; h20=0; e(i,1)=x(i,1)-h10*v(i,1); h(i,1,1)=h10+delta*e(i,1)*v(i,1); h(i,1,2)=h20; for j=2:1:N e(i,j)=x(i,j)-h(i,j-1,1)*v(i,j)-h(i,j-1,2)*v(i,j-1); h(i,j,1)=h(i,j-1,1)+delta*e(i,j)*v(i,j); h(i,j,2)=h(i,j-1,2)+delta*e(i,j)*v(i,j-1); end end for j=1:1:N h1(j)=0; h2(j)=0; for i=1:1:100 h1(j)=h1(j)+h(i,j,1); h2(j)=h2(j)+h(i,j,2); end h1(j)=h1(j)/100; h2(j)=h2(j)/100; end figure(1); plot(h1,'b'); hold on; plot(h2,'r'); grid on; %% i=100; for j=1:1:N f(j)=e(i,j); e1(j)=-a1-h(i,j,2); e2(j)=-a2-h(i,j,1); end figure(2); plot([1:N],abs(fft(f)).^2); figure(3); subplot(2,1,1); plot([1:N],abs(fft(e1)).^2); subplot(2,1,2); plot([1:N],abs(fft(e2)).^2); %% emean=mean(e.^2); %求误差均值 emean(1)=1; emean(2)=1; %理论结果 [V D]=eig([1,-0.5;-0.5,1]); gama=[0.5;1.5]; v1=V'*[-0.1;0.8] v1=v1.^2; for i=1:N J(i)=0.27+gama'*v1; v1=(eye(2)-2*delta*D+delta^2*gama*gama')*v1+delta^2*0.27*gama; end figure(4); plot([1:N],emean(1:N),[1:N],J)