基于matlab实现自适应稳健波束形成对角加载算法，其与输入信噪比的关系.rar

clc;clear; close all; N= 10; %阵元 y = 1; d = y/2; %阵元间距为半波长 as = 0; % 期望信号方向 ai =[-40 50]; e_position = [0:N-1]'; K = 1000; %采样数 快拍数` f0 = 1.5e6; % 信号中心频率 fs=9e6; %采样频率 t = [0:1:K-1]./fs; %采样频率 evrySINR_DLr=[]; snrmax=50; snr=-30; while snr<=snrmax; P0 = 10^(snr/10); %期望信号功率 -160dB P_noise=1; %%噪声功率 P_inr1 = 10^(10/10); %干扰功率 P_inr2 = 10^(10/10); detals=10^(snr/10); w=0; for ii=1:1:100 a0 = exp(j*pi*e_position*sin(0/180*pi)); %期望信号导向矢量 a0_mismatch = exp(j*pi*e_position*sin(5/180*pi)); %失配的期望信号导向矢量 a1 = exp(j*pi*e_position*sin(ai(1)/180*pi)); %干扰信号导向矢量 a2 = exp(j*pi*e_position*sin(ai(2)/180*pi)); %干扰信号导向矢量 s0 = sqrt(2*P0)*sin(2*pi*f0*t); %期望信号 s1 = normrnd(0,sqrt(P_inr1),1,K)+j*normrnd(0,sqrt(P_inr1),1,K); %干扰信号1的复包络,0是均值，sqrt(P_inr1)为标准差，产生1*K维矩阵 s2 = normrnd(0,sqrt(P_inr2),1,K)+j*normrnd(0,sqrt(P_inr2),1,K); %干扰信号1的复包络 noise = randn(N,K)*sqrt(P_noise)+j*randn(N,K)*sqrt(P_noise); %产生均值为0，方差为P_noise的N*K的随机数矩阵 xn = a0*s0+a1*s1+a2*s2+noise; R_x0 = xn*xn'/K; R_x1=R_x0+0.425*eye(10); wop1=inv(R_x1)*a0_mismatch/(a0_mismatch'*inv(R_x1)*a0_mismatch); w=w+wop1; end w=w/100; SINR_DL=detals*(abs(w'*a0))^2/(w'*R_x1*w); SINR_DL=10*log10(SINR_DL) bestr=real(SINR_DL); evrySINR_DLr=[evrySINR_DLr,bestr]; snr=snr+1; end figure(1) plot(-30:1:snrmax,evrySINR_DLr,'-g') legend('DL') xlabel('信噪比 SNR') ylabel('信干燥比 SINR') grid on figure(2) aa =-90:1:90; vv = exp(j*pi*e_position*sin(aa/180*pi)); G=w'*vv; F = abs(G)/max(abs(G)); F0=20*log10(F); plot(aa,F0,'-g'); xlabel('theta') ylabel('波束增益') grid on % legend('SCB')