单元均匀线阵自适应波束形成图.docx

4单元均匀线阵自适应波束形成图clearclcformat long;v=1;M=4;N=1000;f0=21*103;f1=11*103;f2=15*103;omiga0=2*pi*f0;omiga1=2*pi*f1;omiga2=2*pi*f2;sita0=0.8; %信号方向sita1=0.4; %干扰方向1sita2=2.1; %干扰方向2for t=1:N adt(t)=sin(omiga0*t/(N*f0); a1t(t)=sin(omiga1*t/(N*f1); a2t(t)=sin(omiga2*t/(N*f2);endfor i=1:M ad(i,1)=exp(j*(i-1)*pi*sin(sita0); a1(i,1)=exp(j*(i-1)*pi*sin(sita1); a2(i,1)=exp(j*(i-1)*pi*sin(sita2);endR=zeros(M,M);for t=1:N x=adt(t)*ad+a1t(t)*a1+a2t(t)*a2; %阵列对信号的完整响应 R=R+x*x;%信号的协方差矩阵endR=R/N;miu=1/(ad*inv(R)*ad);w=miu*inv(R)*ad;for sita=0:pi/100:pi for i=1:M x_(i,1)=exp(j*(i-1)*pi*sin(sita); end y(1,v)=w*x_; v=v+1;endy_max=max(y(:);y_1=y/y_max;y_db=20*log(y_1); sita=0:pi/100:pi;plot(sita,y)Xlabel（sitaa）Ylabel（天线增益db）4单元均匀线阵自适应波束形成目标clearclcformat long;v=1;M=4;阵元数N=100;f0=21*103;omiga0=2*pi*f0;sita0=0.6;%信号方向 for t=1:N adt(t)=sin(omiga0*t/(N*f0); endfor i=1:M ad(i,1)=exp(j*(i-1)*pi*sin(sita0);endR=zeros(4,4);r=zeros(4,1);for t=1:N x=adt(t)*ad; R=R+x*x.;endR=R/N;miu=1/(ad.*inv(R)*ad);w=miu*inv(R)*ad;for sita=0:pi/100:pi/2 for i=1:M a(i,1)=exp(j*(i-1)*pi*sin(sita); end y(1,v)=w.*a; v=v+1;endsita=0:pi/100:pi/2;plot(sita,y)xlabel(sita)ylabel(天线增益)这是程序全部，有的比较简单的部分就可以省事的，如果解释的好，再加分的，大大的。谢谢了我有更好的答案提问者采纳2010-05-04 18:08你这里有两个程序，第二个程序与第一个实质上是一样的，区别就是信号与导向矢量的写法有点不同，这里我就不注释了。还有，我下面附了一段我自己的写的程序，里面有SIM算法。G-S正交化算法等。是基于圆阵形式的，你的算法是基于线阵的，他们程序上的区别在于导向矢量的不同。我的算法是某项目中的，保证好使。建议学习波束形成技术，注意把程序分块，例如分成，求导向矢量；最优权值；形成波束等等。程序如下：4单元均匀线阵自适应波束形成图clearclcformat long;v=1;M=4;N=1000;%快拍数f0=21*103;%信号与干扰的频率f1=11*103;f2=15*103;omiga0=2*pi*f0;%信号与干扰的角频率omiga1=2*pi*f1;omiga2=2*pi*f2;sita0=0.8; %信号方向sita1=0.4; %干扰方向1sita2=2.1; %干扰方向2for t=1:N %信号 adt(t)=sin(omiga0*t/(N*f0); a1t(t)=sin(omiga1*t/(N*f1); a2t(t)=sin(omiga2*t/(N*f2);endfor i=1:M %信号的导向矢量：线阵的形式 ad(i,1)=exp(j*(i-1)*pi*sin(sita0); a1(i,1)=exp(j*(i-1)*pi*sin(sita1); a2(i,1)=exp(j*(i-1)*pi*sin(sita2);endR=zeros(M,M);for t=1:N x=adt(t)*ad+a1t(t)*a1+a2t(t)*a2; %阵列对信号的完整响应 R=R+x*x;%信号的协方差矩阵endR=R/N;%协方差矩阵，所有快拍数的平均miu=1/(ad*inv(R)*ad);%这个貌似是LMS算法的公式，具体我记不太清，这里是求最优权值，根据这个公式求出，然后加权w=miu*inv(R)*ad;%形成波束%for sita=0:pi/100:pi for i=1:M x_(i,1)=exp(j*(i-1)*pi*sin(sita); end y(1,v)=w*x_;%对信号进行加权，消除干扰 v=v+1;endy_max=max(y(:);%归一化y_1=y/y_max;y_db=20*log(y_1); sita=0:pi/100:pi;plot(sita,y)Xlabel（sitaa）Ylabel（天线增益db）4单元均匀线阵自适应波束形成目标clearclcformat long;v=1;M=4;阵元数N=100;f0=21*103;omiga0=2*pi*f0;sita0=0.6;%信号方向 for t=1:N adt(t)=sin(omiga0*t/(N*f0); endfor i=1:M ad(i,1)=exp(j*(i-1)*pi*sin(sita0);endR=zeros(4,4);r=zeros(4,1);for t=1:N x=adt(t)*ad; R=R+x*x.;endR=R/N;miu=1/(ad.*inv(R)*ad);w=miu*inv(R)*ad;for sita=0:pi/100:pi/2 for i=1:M a(i,1)=exp(j*(i-1)*pi*sin(sita); end y(1,v)=w.*a; v=v+1;endsita=0:pi/100:pi/2;plot(sita,y)xlabel(sita)ylabel(天线增益)%我的程序%function jieshousignal%期望信号数：1个%干扰信号数：4个%信噪比已知%干燥比已知%方位角已知clc;clear all;close all;%/参数设置=www1=0;www2=0;www3=0;% for rrr=1:16000signal_num=1; %signal numbernoise_num=5; %interference numberR0=0.6; %圆的半径SP=2000; %Sample numberN=8; %阵元数snr=-10; %Signal-to-Noisesir1=10; %Signal-to-Interference one sir2=10; %Signal-to-Interference two sir3=10; %Signal-to-Interfsir4=10;sir5=10;%/=noise Power-to-signal Power= factor_noise_1=10.(-sir1/10);factor_noise_2=10.(-sir2/10);factor_noise_3=10.(-sir3/10);factor_noise_4=10.(-sir4/10);factor_noise_5=10.(-sir5/10);factor_noise_targe=10.(-snr/10);% /= =d1=85*pi/180;%干扰1的方位角d2=100*pi/180;%干扰2的方位角d3=147*pi/180;%干扰3的方位角d4=200*pi/180;%干扰4的方位角d5=250*pi/180;%干扰5的方位角d6=150*pi/180;%目标的方位角e1=15*pi/180;%干扰1的俯仰角e2=25*pi/180;%干扰2的俯仰角e3=85*pi/180;%干扰3的俯仰角e4=50*pi/180;%干扰4的俯仰角e5=70*pi/180;%干扰5的俯仰角e6=85*pi/180;%目标的俯仰角% /=目标信号=t=1:1:SP;fc=2e7;Ts=1/(3e10);S0=5*cos(2*pi*fc*t*Ts);%目标信号for kk=1:N phi_n(kk)=2*pi*(kk-1)/N;end%/=操纵矢量=A=conj(exp(j*2*pi*R0*cos(d6-phi_n)*sin(e6);conj(exp(j*2*pi*R0*cos(d1-phi_n)*sin(e1);conj(exp(j*2*pi*R0*cos(d2-phi_n)*sin(e2);conj(exp(j*2*pi*R0*cos(d3-phi_n)*sin(e3);conj(exp(j*2*pi*R0*cos(d4-phi_n)*sin(e4);conj(exp(j*2*pi*R0*cos(d5-phi_n)*sin(e5);A1=conj(exp(j*2*pi*R0*cos(d1-phi_n)*sin(e1);conj(exp(j*2*pi*R0*cos(d2-phi_n)*sin(e2);conj(exp(j*2*pi*R0*cos(d3-phi_n)*sin(e3);conj(exp(j*2*pi*R0*cos(d4-phi_n)*sin(e4);conj(exp(j*2*pi*R0*cos(d5-phi_n)*sin(e5);% /=Power of the interference % / depending on the signal power and SIRPs1=0;Ps2=0;Ps3=0;Ps4=0;Ps5=0;S1=zeros(1,SP);S2=zeros(1,SP);S3=zeros(1,SP);S4=zeros(1,SP);S5=zeros(1,SP);Ps0=S0*S0/SP; % signal powerPs1=Ps0*factor_noise_1;Ps2=Ps0*factor_noise_2;Ps3=Ps0*factor_noise_3;Ps4=Ps0*factor_noise_4;Ps5=Ps0*factor_noise_5;% /=干扰信号的随机包络=S1=normrnd(0,sqrt(Ps1/2),1,SP)+j*normrnd(0,sqrt(Ps1/2),1,SP);S2=normrnd(0,sqrt(Ps2/2),1,SP)+j*normrnd(0,sqrt(Ps2/2),1,SP);S3=normrnd(0,sqrt(Ps3/2),1,SP)+j*normrnd(0,sqrt(Ps3/2),1,SP);S4=normrnd(0,sqrt(Ps4/2),1,SP)+j*normrnd(0,sqrt(Ps4/2),1,SP);S5=normrnd(0,sqrt(Ps5/2),1,SP)+j*normrnd(0,sqrt(Ps5/2),1,SP);%/S=S0;S1;S2;S3;S4;S5;SS1=S1;S2;S3;S4;S5; X=A*S;%信号加干扰 XX2=A1*SS1; %接收到的干扰 Pw_noise=sqrt(Ps0*factor_noise_targe/2); a1=randn(N,SP); a2=randn(N,SP); a1=a1/norm(a1); a2=a2/norm(a2); W=Pw_noise*(a1+sqrt(-1)*a2); X=X+W;% /-SMI算法-Rd=X*S0/SP;R=X*X/(SP*1);Wc_SMI=pinv(R)*Rd./(Rd*pinv(R)*Rd);%权向量Wc_SMI=Wc_SMI/norm(Wc_SMI);Y_SMI=Wc_SMI*X; %SMI算法恢复出来的信号%/-GS算法-m=1;for i=1:400:2000 X2(:,m)=XX2(:,i); m=m+1;enda=zeros(1,8);phi_n=zeros(1,8);phi=0:pi/180:2*pi;theta=0:pi/180:pi/2;for kk=1:8 a(kk)=1; phi_n(kk)=2*pi*(kk-1)/8;endx1=zeros(1,8);x2=zeros(1,8);x3=zeros(1,8);x4=zeros(1,8);x5=zeros(1,8);x1=X2(:,1);x2=X2(:,2);x3=X2(:,3);x4=X2(:,4);x5=X2(:,5);Z1=x1;Z1_inner_product=Z1.*conj(Z1);Z1_mode=sqrt(sum(Z1_inner_product);Y1=Z1./Z1_mode;Inner_product=sum(x2.*conj(Y1);Z2=x2-Inner_product*Y1;Z2_inner_product=sum(Z2.*conj(Z2);Z2_mode=sqrt(Z2_inner_product);Y2=Z2./Z2_mode;Inner_product1=sum(x3.*conj(Y1);Inner_product2=sum(x3.*conj(Y2);Z3=x3-Inner_product1*Y1-Inner_product2*Y2;Z3_inner_product=sum(Z3.*conj(Z3);Z3_mode=sqrt(Z3_inner_product);Y3=Z3./Z3_mode;Inner_product1_0=sum(x4.*conj(Y1);Inner_product2_0=sum(x4.*conj(Y2);Inner_product3_0=sum(x4.*conj(Y3);Z4=x4-Inner_product1_0*Y1-Inner_product2_0*Y2-Inner_product3_0*Y3;Z4_inner_product=sum(Z4.*conj(Z4);Z4_mode=sqrt(Z4_inner_product);Y4=Z4./Z4_mode;Inner_product1_1=sum(x5.*conj(Y1);Inner_product2_1=sum(x5.*conj(Y2);Inner_product3_1=sum(x5.*conj(Y3);Inner_product4_1=sum(x5.*conj(Y4);Z5=x5-Inner_product1_1*Y1-Inner_product2_1*Y2-Inner_product3_1*Y3-Inner_product4_1*Y4;Z5_inner_product=sum(Z5.*conj(Z5);Z5_mode=sqrt(Z5_inner_product);Y5=Z5./Z5_mode;%Y1%Y2%Y3%Y4%Y5 w0=zeros(1,8); w=zeros(1,8); for mm=1:8; w0(mm)=exp(-j*2*pi*R0*cos(d6-phi_n(mm)*sin(e6); end dd1=sum(w0.*conj(Y1)*Y1; dd2=sum(w0.*conj(Y2)*Y2; dd3=sum(w0.*conj(Y3)*Y3; dd4=sum(w0.*conj(Y4)*Y4; dd5=sum(w0.*conj(Y5)*Y5; w=w0-dd1-dd2-dd3-dd4-dd5;Wc_GS=w;Wc_GS=Wc_GS/(norm(Wc_GS);Y_GS=Wc_GS*X; %GS算法恢复出来的图像%/-MMSE算法-Rd=X*S0/SP;R=X*X/(SP*1);Wc_MMSE=pinv(R)*Rd;Wc_MMSE=Wc_MMSE/norm(Wc_MMSE);Y_MMSE=Wc_MMSE*X; %MMSE算法恢复出来的信号S0=S0/norm(S0);Y_GS=Y_GS/norm(Y_GS);Y_SMI=Y_SMI/norm(Y_SMI);Y_MMSE=Y_MMSE/norm(Y_MMSE);% figure(1)% plot(real(S0);% title(原始信号);% xlabel(采样快拍数);% ylabel(信号幅度);% figure(2)% plot(real(Y_SMI);% title(运用SMI算法处理出的信号);% xlabel(采样快拍数);% ylabel(信号幅度);% figure(3)% plot(real(Y_GS);% title(运用G-S算法处理出的信号);% xlabel(采样快拍数);% ylabel(信号幅度);% figure(4)% plot(real(Y_MMSE);% for i=1:SP% ss(i)=abs(S0(i)-Y_SMI(i)2;% end% q_1=mean(ss);% for i=1:SP% ss1(i)=abs(S0(i)-Y_GS(i)2;% end% q_2=mean(ss1);% for i=1:SP% ss2(i)=abs(S0(i)-Y_MMSE(i)2;% end% q_3=mean(ss2);% % www1=www1+q_1;% www2=www2+q_2;% www3=www3+q_3;% end% www1