% function [pattern1,pattern0]=LCMV(N,d_lamda,theta0,thetaj,theta,SNR,SJR,Ns)
%
clear all;close all;clc
N=20;d_lamda=0.5;theta0=0;thetaj=[20 40];
theta=-60:1:60;SNR=20;SJR=40;Ns=32;
nj=length(thetaj);
eps=0.00001;
a0=exp(j*2*pi*d_lamda*(0:N-1)'*sin(theta0*pi/180));
aj=exp(j*2*pi*d_lamda*(0:N-1)'*sin(thetaj*pi/180));
atheta=exp(j*2*pi*d_lamda*(0:N-1)'*sin(theta*pi/180));
noise=10^(SNR/20)*0.707*(randn(N,Ns)+j*randn(N,Ns));
Aj=10^(SJR/20)*0.707*(randn(nj,Ns)+j*randn(nj,Ns));
X=aj*Aj+noise;
R=i/Ns*(X*X');
R_inv=inv(R);
Wopt=R_inv*a0;
% W_opt=R_inv*a0./(a0'*R_inv*a0);
pattern1=abs(Wopt'*atheta);
pattern1=20*log10(pattern1/max(pattern1));
pattern0=abs(a0'*atheta)+eps;
pattern0=20*log10(pattern0/max(pattern0));
plot(theta,pattern0,'r--',theta,pattern1);
grid on;xlabel('\fontsize{14}方位角/\circ');ylabel('\fontsize{14}增益/dB');ylim([-80 0]);
title('\fontsize{14}基于最小方差准则（LCMV）的仿真结果');
% end
