How to separate the MIMO code only from given code?

Question

Sadiq Akbar 2021-10-9

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1470041-how-to-separate-the-mimo-code-only-from-given-code

The given code treats three types of system namly PHASED-ARRAY RADAR, MIMO RADAR and PHASED-MIMO RADAR (K=5). I want to delete the two namely PHASED-ARRAY RADAR and PHASED-MIMO RADAR (K=5) and keep only the code of MIMO RADAR and run it to get only its plot. But how as I am confused in doing so. Can anybody help me in this regard?

% subarrays that can overlap
function [sinr_o] = phased_mimo_radar()
clear;clc;
M = 10;                                           % Total number of transmitting antennas
M_r = 10;                                         % Total number of receiving antennas
d_t = 0.5;                                        % transmitter spacings in wavelength
theta_tar = 10*pi/180;                            % Direction of target is 10 degress to the broadside of the array
a_tar = exp(-j*d_t*2*pi*(0:M-1)'*sin(theta_tar));  % Uplink steering vector
b_tar = exp(-j*pi*(0:M_r-1)'*sin(theta_tar));      % Downlink steering vector; Tx and Rx are assumed to be close to each other
theta_intrf = [-30 -10]*pi/180;                   % Interference dirstions
a_intrf = exp(-j*d_t*2*pi * (0:M-1)'*sin(theta_intrf));% steering vectors of interference
b_intrf = exp(-j*pi * (0:M_r-1)'*sin(theta_intrf));
%
no_subarrays = [1 5 10];  % K = 1, 5, and 10 correspond to phased-array, phased-MIMO, and MIMO radars, respectively
P_noise = 1;
INR = 50;
P_intrf = 10^(INR/10);
SNR = -0:10:0;
L_snr = length(SNR);
sinr_o = zeros(L_snr,length(no_subarrays));
Tx_pattern_conv = [];                           % Transmit beampattern
Dx_pattern_conv = [];                           % Diversity beampattern
Rx_pattern_conv = [];                           % Overall beampattern
for ksub = 1:length(no_subarrays);
    K_sub = no_subarrays(ksub);
    M_sub = M - K_sub + 1 ;                      % Number of antennas in each subarray
    W_u_conv = uplink_conventional_beamforming(a_tar, K_sub, M_sub);
    % Computing Transmit and diversity beampatterns
    Theta_grid = [linspace(-pi/2,pi/2,1801)];
    Tx_grid = exp(-j*d_t*2*pi * (0:M-1)'*sin(Theta_grid));
    kk = 1; % kk refers to the k-th subarray, i.e., kk=1,....,K. Note that the K subarrays are identical. So the transmit beampattern is identical for all of them.
    Tx_pattern =  abs(W_u_conv(:,kk)'*Tx_grid(kk:M_sub + kk-1,:)).^2;
    Tx_pattern = 10*log10(Tx_pattern);Tx_pattern = Tx_pattern - max(Tx_pattern);
    Tx_pattern_conv = [Tx_pattern_conv; Tx_pattern];
    Dx_pattern = abs(a_tar(1:K_sub)'*Tx_grid(1:K_sub,:)).^2;
    Dx_pattern = 10*log10(Dx_pattern);Dx_pattern = Dx_pattern - max(Dx_pattern);
    Dx_pattern_conv = [Dx_pattern_conv; Dx_pattern];
    % Compute virtual steering vectors of target/interference
    [v_tar] = virtual_sv(theta_tar, M, d_t, M_r, M_sub, K_sub, W_u_conv);
    [v_intrf] = virtual_sv(theta_intrf, M, d_t, M_r, M_sub, K_sub, W_u_conv);%required for optimal SINR_o
    for snr = 1:L_snr, snr
        P_tar = 10^(SNR(snr)/10);
        %P_intrf = P_tar;   % keeping target and interference at the same power level
        % Compute signal model using conventional uplink beamforming
        [R] = MIMO_radar_signal_model(K_sub, a_tar, a_intrf, b_tar, b_intrf, P_tar, P_intrf, W_u_conv);
        w_d_conv = v_tar/(norm(v_tar));     % conventional downlink beamformer
        % output SINR for conventional Tx/Rx phased-MIMO radar
        SINR_conv(snr,ksub) = 10*log10((M/K_sub)*P_tar*(abs(w_d_conv'*(v_tar)))^2/real(w_d_conv'*((M/K_sub)*P_intrf*v_intrf*v_intrf'+ P_noise*eye(K_sub*M_r))*w_d_conv));
        % MVDR beamformer
        w_d_capon = Capon_down_beamforming(v_tar, R); 
        % Compute and plot overall Tx/Rx beampattern
        w_d = w_d_conv; % Use this to plot overall beampattern for Example 1 
        w_d = w_d_capon; % uncomment this line to plot overall beampattern for Example 5 and 6
        [V_grid] = virtual_sv(Theta_grid, M, d_t, M_r, M_sub, K_sub, W_u_conv);
        Rx_pattern = [10*log10(abs(w_d'*V_grid).^2)];
        Rx_pattern = Rx_pattern - max(Rx_pattern);
        Rx_pattern_conv = [Rx_pattern_conv; Rx_pattern];
        %
        SINR_MVDR(snr,ksub) = 10*log10((M/K_sub)*P_tar*(abs(w_d_capon'*(v_tar)))^2/real(w_d_capon'*((M/K_sub)*P_intrf*v_intrf*v_intrf'+ P_noise*eye(K_sub*M_r))*w_d_capon));        
        % Optimal output SINR
        R_intrf_noise = (M/K_sub)*P_intrf * v_intrf*v_intrf' +  P_noise*eye(K_sub*M_r);
        R_intrf_noise = inv(R_intrf_noise);
        w_opt = (1/(v_tar' * R_intrf_noise * v_tar)) * R_intrf_noise * v_tar;
        SINR_opt(snr,ksub) = 10*log10((M/K_sub)*P_tar*(abs(w_opt'*(v_tar)))^2/real(w_opt'*((M/K_sub)*P_intrf*v_intrf*v_intrf'+ P_noise*eye(K_sub*M_r))*w_opt));
    end
end
%
Theta = Theta_grid;
plot(Theta*180/pi,1.0*Tx_pattern_conv(1,:),'m--',Theta*180/pi,1.0*Tx_pattern_conv(3,:),'r:',Theta*180/pi,Tx_pattern_conv(2,:),'b','linewidth',1),grid
axis([-90 90 -80 20]),xlabel('ANGLE (DEGREES)'),ylabel('|C(\theta)|^2 (dB)')
legend('PHASED-ARRAY RADAR','MIMO RADAR','PHASED-MIMO RADAR (K=5)')
% % %
figure
Theta = Theta_grid;
plot(Theta*180/pi,1.0*Dx_pattern_conv(1,:),'m--',Theta*180/pi,1.0*Dx_pattern_conv(3,:),'r:',Theta*180/pi,Dx_pattern_conv(2,:),'b','linewidth',1),grid
axis([-90 90 -80 20]),xlabel('ANGLE (DEGREES)'),ylabel('|D(\theta)|^2 (dB)')
legend('PHASED-ARRAY RADAR','MIMO RADAR','PHASED-MIMO RADAR (K=5)')
% % %
%Rx_pattern_conv = Tx_pattern_conv+Dx_pattern_conv;
figure
plot(Theta*180/pi,1.02*Rx_pattern_conv(1,:),'m--',Theta*180/pi,1.0*Rx_pattern_conv(3,:),'r:',Theta*180/pi,Rx_pattern_conv(2,:),'b','linewidth',1),grid
axis([-90 90 -120 30]),xlabel('ANGLE (DEGREES)'),ylabel('|G(\theta)|^2 (dB)')
legend('PHASED-ARRAY RADAR','MIMO RADAR','PHASED-MIMO RADAR (K=5)')
%--------------------------------------------------------------------------
% This function computes the orthogonal waveforms that are emitted by
% transmitter
%--------------------------------------------------------------------------
function [R] = MIMO_radar_signal_model(K_sub, a_tar, a_intrf, b_tar, b_intrf, P_tar, P_intrf, W_u)
M_t = length(a_tar);
M_r = length(b_tar);
M_sub = M_t - K_sub + 1;
L_intrf = length(a_intrf(1,:));
% ---------- Orthogonal waveforms ------
N = 400; % number of smaples within one radar pulse (fast-time)
phi = [];
for kk = 1:K_sub
    phi = [phi; exp(j*2*pi*(kk/N)*(0:N-1))];
end
% compute signals reflect by targets (observed by receiver)
T = 200; % number of radar pulses (slow-time)
y = zeros(K_sub*M_r,T); % K_sub matched filter applied to each antenna output (y is of size K_sub*N \times T)
Beta_tar = sqrt(P_tar)* (randn(1,T)+1i*randn(1,T));
Beta_intrf = sqrt(P_intrf)* (randn(L_intrf,T)+1i*randn(L_intrf,T));
for tt = 1:T;   % tt is the snapshot number
    % compute signals observed at targets
    x_tar = zeros(1,N);
    x_intrf = zeros(L_intrf,N);
    for kk = 1:K_sub
        x_tar = x_tar + Beta_tar(tt)*(W_u(:,kk)'* a_tar(kk:M_sub + kk-1))* phi(kk,:);
        for ii = 1:L_intrf
            x_intrf(ii,:) = x_intrf(ii,:) + Beta_intrf(ii,tt)*(W_u(:,kk)'* a_intrf(kk:M_sub + kk-1,ii))*phi(kk,:);
        end
        Z = (randn(M_r,N)+1i*randn(M_r,N));
        x1 = b_tar * x_tar;
        x2 =  b_intrf * x_intrf;
        x =  x2 + Z;
        y((kk-1)*M_r+1:kk*M_r,tt) = (1/N)*x*conj(phi(kk,:).');
    end
end
R = (1/T)*(y*y');
%--------------------------------------------------------------------------
% This function computes the virtual steering vectors
%--------------------------------------------------------------------------
function [v_sv] = virtual_sv(Theta, Mt, d_t, Mr, M_sub, no_subarrays, W_u);
Tx_sv = exp(-j*d_t*2*pi * (0:Mt-1)'*sin(Theta));
Rx_sv = exp(-j*pi * (0:Mr-1)'*sin(Theta)); % Actual receiving steering vectors
v_sv = [];
for kk = 1:no_subarrays
    v_temp = [];
    w_u = W_u(:,kk);
    for jj = 1:length(Theta)
        %abs(w_u'*a_intrf(i:M_sub + i-1, jj))
        v_temp = [v_temp, (w_u'*Tx_sv(kk:M_sub + kk-1, jj))*Rx_sv(:,jj)];
    end
    v_sv = [v_sv; v_temp];
end
%--------------------------------------------------------------------------
% This function computes the the uplink beamforming matrix. The kth column
% respresents the beamforming weight vector of the kth subarray
%--------------------------------------------------------------------------
function [W_u] = uplink_conventional_beamforming(a_tar, K_sub, M_sub);
w_u = a_tar(1:M_sub);                   % Uplind weight vector
w_u = w_u/(norm(w_u));
W_u = kron(ones(1, K_sub), w_u);
%--------------------------------------------------------------------------
% This function computes the the downlink beamforming weight vector
% (virtual array) using capon method.
%--------------------------------------------------------------------------
function [w_d_capon] = Capon_down_beamforming(v_tar, R);
R_y = inv(R);
w_d_capon = (1/(v_tar' * R_y * v_tar)) * R_y * v_tar;