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m基于matlab的超宽带MIMO雷达对目标的检测仿真,考虑时间反转

2022-07-17 05:22:00 【我爱C编程】

1.算法描述

2.部分程序

3.部分仿真图预览

4.源码获取方式

1.算法描述

（不加时间反转处理）参看框图1：天线阵A发送信号，经过目标场，在接收阵B端接收数据记为Y1，然后对所接收到的信号处理（匹配滤波等处理过程），得到回波的信噪比，目标的位置及成像；用图示表示如下：

不加时间反转处理的程序，其基本流程图如下所示：

（加上时间反转处理）在B端接收到信号Y1，对Y1做时间反转处理，能量归一化后再发射出去，经过同样的空间场，然后在A端接收数据记为Y2，对接收到的数据Y2再做处理（匹配滤波等），得到回波的信噪比（与第一步未加时间反转做对比），目标的位置信息及成像。用图示表示如下：

加时间反转处理的程序，其基本流程图如下所示：

2.部分程序


tic
%产生超宽带信号，高斯脉冲信号
%中心频率为：900M，频率范围：500M----1300M
E0    = 1;
alpha = [0 0.75 1.5 3];
fc    = 900e6; %中心频率
f1    = 500e6; %范围
f2    = 1300e6;%范围
k     = 1e6;   %显示缩小倍数，如果不缩小，则设置为1，但是为了能够看到实际的脉冲信号效果，则设置1e6
scale = 0.05;   %plot显示精度

figure;
color = ['b','g','r','k'];
for i   = 1:length(alpha)
    [gt,f]  = func_UWB_gspluse(E0,fc,f1,f2,k,scale,alpha(i));
    tmp     = color(i);
    plot(f,gt,tmp);
    hold on;
    axis([898,902,-0.5,1.2]);
end
title('超宽带高斯脉冲信号');
legend('alpha = 0','alpha = 0.75','alpha = 1.5','alpha = 3');
% clear E0 alpha fc f1 f2 k scale color gt f
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%进行传统算法
%下面开始实际的算法
%这里发送天线和接收天线设置为4和1，如果要修改
%%
%参数的初始化
Nt   = 3;
Nr   = 3;
L    = length(gt);
l    = 1:L;
T    = 1/(10^6);
ts   = T/L;
SNR  =-10:1:20;
times= 200;
Pfa  = 10e-5;%虚警概率
%%
%超带宽高斯脉冲信号
[gt,f]  = func_UWB_gspluse(E0,fc,f1,f2,k,scale,alpha(1));

%MIMO发送信号
X = func_MIMO_trans(gt,Nt,ts,T);
ind = 0;
for j = 1:length(SNR)
    N0    = 10^(SNR(j)/10);
    count = 0;
    mui   = 1;
    for i = 1:times%利用门特卡罗的设计仿真思想，计算不同信噪比下的检测概率
        ind = ind + 1 
        %通过MIMO信道
        [alpha,H]=func_MIMO_channel(Nt,N0);
        %接收机接收到的信号
        for l=1:L           
            r(:,l)=H*X(:,l);
        end 
        
        %将时间反转的信号通过信道发送回去
        for l=1:L      
            r2(:,l)=H*conj(r(:,l));
        end         

        %匹配滤波
        [E,R] = func_match_filter(r2,X,L);
        %目标检测 
        Theta_doa = func_MIMORadar_DOA(E,R);
        %判断是否被检测到
        threshold = 10000;
        if(Theta_doa>threshold)
           detected = 1;
           count    = count + 1;
        else
           p = rand(1,1);
           if p > 1-Pfa    %虚警概率
              detected = 1;
              count    = count + 1;
           else
              detected = 0;
           end
        end
    end
    pc(j) = count/times;
end
toc
figure;
plot(SNR,pc,'b-o');        
xlabel('SNR');
ylabel('Pmd');
grid on;