上传文件至「Graduation Design Matlab」

Graduation Design Matlab/RMSE_Test.m

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+%% 全空域四扇区动态先验鲁棒性测试 (多核并行加速版)
+clear; clc; close all;
+rng(2024); 
+
+par = setpar(); 
+N_ant = par.M; N_RF = par.Nrf;
+clutter_angle = -15; a_clutter = gen_a(clutter_angle, par);
+INR_linear = 10^(30/10); noise_power = 1;
+R_interference = noise_power * eye(N_ant) + INR_linear * (a_clutter * a_clutter');
+
+disp('正在构建全空域字典 (传统OMP使用)...');
+dic_angles_full = -90:0.5:90; A_dic_full = zeros(N_ant, length(dic_angles_full));
+for i = 1:length(dic_angles_full), A_dic_full(:,i) = gen_a(dic_angles_full(i), par) / sqrt(N_ant); end
+
+angle_ranges = {0:1:10, 11:1:20, 21:1:30, 31:1:40};
+snr_test_list = [0, 5, 10, 15, 20]; 
+Monte_Carlo = 500;                  
+L_snapshots = 100;
+color_array = {'#0072BD', '#D95319', '#EDB120', '#7E2F8E', '#77AC30'}; 
+
+disp('正在启动多核并行引擎 (parfor) 进行全空域极速测试...');
+for fig_idx = 1:4
+    test_angles = angle_ranges{fig_idx};
+    fprintf('\n>>> 正在进行第 %d 张图的并行仿真：扇区 %d° ~ %d° ...\n', fig_idx, min(test_angles), max(test_angles));
+    
+    RMSE_DFT_All = zeros(length(snr_test_list), length(test_angles));
+    RMSE_OMP_FULL_All = zeros(length(snr_test_list), length(test_angles));
+    RMSE_OMP_All = zeros(length(snr_test_list), length(test_angles));
+    
+    figure('Name', sprintf('扇区 %d°~%d° 测试', min(test_angles), max(test_angles)), 'Color', 'w', 'Position', [50+fig_idx*50, 50+fig_idx*50, 850, 600]);
+    hold on; grid on;
+    
+    for snr_idx = 1:length(snr_test_list)
+        snr_test = snr_test_list(snr_idx);
+        sig_power = 10^(snr_test/10); 
+        
+        err_dft = zeros(length(test_angles), Monte_Carlo);
+        err_omp_full = zeros(length(test_angles), Monte_Carlo);
+        err_omp = zeros(length(test_angles), Monte_Carlo);
+        
+        for a_idx = 1:length(test_angles)
+            true_target_angle = test_angles(a_idx) + 0.04; 
+            theta_prior = round(true_target_angle); window = 5; 
+            
+            dic_tsc = (theta_prior - window) : 0.1 : (theta_prior + window);
+            A_dic_tsc = zeros(N_ant, length(dic_tsc));
+            for i = 1:length(dic_tsc), A_dic_tsc(:,i) = gen_a(dic_tsc(i), par) / sqrt(N_ant); end
+            
+            angles_dft = linspace(theta_prior - window, theta_prior + window, N_RF);
+            F_RF_dft = zeros(N_ant, N_RF);
+            for i = 1:N_RF, F_RF_dft(:,i) = gen_a(angles_dft(i), par) / sqrt(N_ant); end
+            
+            a_prior_broad = zeros(N_ant, 1);
+            for theta_b = (theta_prior - window) : 0.5 : (theta_prior + window)
+                a_prior_broad = a_prior_broad + gen_a(theta_b, par);
+            end
+            a_prior_broad = a_prior_broad / norm(a_prior_broad);
+            w_dig_prior = inv(R_interference) * a_prior_broad; w_dig_prior = w_dig_prior / norm(w_dig_prior);
+            
+            res_full = w_dig_prior; F_RF_omp_full = [];
+            for k = 1:N_RF
+                proj = A_dic_full' * res_full; [~, midx] = max(abs(proj));
+                F_RF_omp_full = [F_RF_omp_full, A_dic_full(:, midx)]; res_full = w_dig_prior - F_RF_omp_full * (pinv(F_RF_omp_full)*w_dig_prior);
+            end
+            res_tsc = w_dig_prior; F_RF_omp = [];
+            for k = 1:N_RF
+                proj = A_dic_tsc' * res_tsc; [~, midx] = max(abs(proj));
+                F_RF_omp = [F_RF_omp, A_dic_tsc(:, midx)]; res_tsc = w_dig_prior - F_RF_omp * (pinv(F_RF_omp)*w_dig_prior);
+            end
+            
+            scan_grid = (theta_prior - window) : 0.01 : (theta_prior + window);
+            
+            % 为 parfor 准备的切片临时变量
+            err_dft_tmp = zeros(1, Monte_Carlo); err_omp_full_tmp = zeros(1, Monte_Carlo); err_omp_tmp = zeros(1, Monte_Carlo);
+            
+            % 【核心提速】：parfor 多核并行循环！
+            parfor mc = 1:Monte_Carlo
+                s_t = sqrt(sig_power/2)*(randn(1,L_snapshots) + 1j*randn(1,L_snapshots));
+                s_c = sqrt(INR_linear/2)*(randn(1,L_snapshots) + 1j*randn(1,L_snapshots));
+                n_t = sqrt(noise_power/2)*(randn(N_ant,L_snapshots) + 1j*randn(N_ant,L_snapshots));
+                X = gen_a(true_target_angle, par)*s_t + a_clutter*s_c + n_t;
+                
+                Y_dft = F_RF_dft' * X; Y_omp_full = F_RF_omp_full' * X; Y_omp = F_RF_omp' * X;
+                R_dft = (Y_dft*Y_dft')/L_snapshots; R_dft = R_dft + (0.05*trace(R_dft)/N_RF)*eye(N_RF);
+                R_omp_full = (Y_omp_full*Y_omp_full')/L_snapshots; R_omp_full = R_omp_full + (0.05*trace(R_omp_full)/N_RF)*eye(N_RF);
+                R_omp = (Y_omp*Y_omp')/L_snapshots; R_omp = R_omp + (0.05*trace(R_omp)/N_RF)*eye(N_RF);
+                
+                inv_dft = inv(R_dft); inv_full = inv(R_omp_full); inv_tsc = inv(R_omp);
+                P_dft = zeros(1, length(scan_grid)); P_full = zeros(1, length(scan_grid)); P_tsc = zeros(1, length(scan_grid));
+                
+                for s_idx = 1:length(scan_grid)
+                    a_test = gen_a(scan_grid(s_idx), par);
+                    P_dft(s_idx) = 1/abs((F_RF_dft'*a_test)'*inv_dft*(F_RF_dft'*a_test));
+                    P_full(s_idx) = 1/abs((F_RF_omp_full'*a_test)'*inv_full*(F_RF_omp_full'*a_test));
+                    P_tsc(s_idx) = 1/abs((F_RF_omp'*a_test)'*inv_tsc*(F_RF_omp'*a_test));
+                end
+                
+                [~, m1] = max(P_dft); err_dft_tmp(mc) = (scan_grid(m1) - true_target_angle)^2;
+                [~, m2] = max(P_full); err_omp_full_tmp(mc) = (scan_grid(m2) - true_target_angle)^2;
+                [~, m3] = max(P_tsc); err_omp_tmp(mc) = (scan_grid(m3) - true_target_angle)^2;
+            end
+            err_dft(a_idx, :) = err_dft_tmp; err_omp_full(a_idx, :) = err_omp_full_tmp; err_omp(a_idx, :) = err_omp_tmp;
+        end
+        RMSE_DFT_All(snr_idx, :) = sqrt(mean(err_dft, 2))'; RMSE_OMP_FULL_All(snr_idx, :) = sqrt(mean(err_omp_full, 2))'; RMSE_OMP_All(snr_idx, :) = sqrt(mean(err_omp, 2))';
+        
+        c_str = color_array{snr_idx};
+        semilogy(test_angles, RMSE_DFT_All(snr_idx, :), '--^', 'Color', c_str, 'LineWidth', 1.5, 'MarkerSize', 5);
+        semilogy(test_angles, RMSE_OMP_FULL_All(snr_idx, :), ':x', 'Color', c_str, 'LineWidth', 2, 'MarkerSize', 7);
+        semilogy(test_angles, RMSE_OMP_All(snr_idx, :), '-o', 'Color', c_str, 'LineWidth', 2.5, 'MarkerSize', 6);
+    end
+    xlim([min(test_angles), max(test_angles)]); xticks(min(test_angles):1:max(test_angles));
+    set(gca, 'YScale', 'log', 'YMinorGrid', 'on', 'XMinorGrid', 'off', 'FontSize', 12); ylim([1e-3, 20]); yticks(10.^(-3:1:2));         
+    xlabel('目标真实出现角度 \theta (\circ)', 'FontSize', 14, 'FontWeight', 'bold'); ylabel('测角均方根误差 RMSE (\circ)', 'FontSize', 14, 'FontWeight', 'bold'); title(sprintf('宽波束先验引导下，扇区 (%d°~%d°) 动态波门鲁棒性评估', min(test_angles), max(test_angles)), 'FontSize', 15, 'FontWeight', 'bold');
+    
+    legend_str = {};
+    for snr_idx = 1:length(snr_test_list)
+        legend_str{end+1} = sprintf('SNR=%ddB, DFT', snr_test_list(snr_idx)); legend_str{end+1} = sprintf('SNR=%ddB, 传统OMP(发散)', snr_test_list(snr_idx)); legend_str{end+1} = sprintf('SNR=%ddB, TSC-OMP(稳健)', snr_test_list(snr_idx));
+    end
+    lgd = legend(legend_str, 'Location', 'southoutside', 'FontSize', 10); set(lgd, 'NumColumns', 3); 
+end
+disp('全部四组扇区极速测试完毕！');

Graduation Design Matlab/main.m

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+%% 大规模阵列 MIMO 数模混合波束成形综合仿真平台 (6核全自动并行加速版)
+clear; clc; close all;
+rng(2024); % 锁定随机数种子
+
+%% 1. 系统参数与环境初始化
+par = setpar(); 
+N_ant = par.M; N_RF = par.Nrf;
+target_angle = 5;    
+clutter_angle = -15;    
+theta_scan = -90:0.1:90; 
+
+a_target = gen_a(target_angle, par);
+a_clutter = gen_a(clutter_angle, par);
+L_snapshots = 100; noise_power = 1; INR_linear = 10^(30/10); 
+
+c_ana = '#77AC30'; c_dig = '#000000'; c_dft = '#4DBEEE'; c_omp = '#D95319'; c_tsc = '#0072BD'; 
+
+%% 2. 动态生成目标约束扇区与字典
+theta_coarse_prior = round(target_angle); window_half_width = 5; 
+dic_angles_constrained = (theta_coarse_prior - window_half_width) : 0.1 : (theta_coarse_prior + window_half_width);
+A_dic_constrained = zeros(N_ant, length(dic_angles_constrained));
+for i = 1:length(dic_angles_constrained)
+    A_dic_constrained(:,i) = gen_a(dic_angles_constrained(i), par) / sqrt(N_ant);
+end
+dic_angles_full = -90:0.5:90;
+A_dic_full = zeros(N_ant, length(dic_angles_full));
+for i = 1:length(dic_angles_full)
+    A_dic_full(:,i) = gen_a(dic_angles_full(i), par) / sqrt(N_ant);
+end
+F_RF_dft = zeros(N_ant, N_RF);
+angles_dft = linspace(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width, N_RF);
+for i = 1:N_RF
+    F_RF_dft(:,i) = gen_a(angles_dft(i), par)/sqrt(N_ant);
+end
+
+%% 3. 提取防发散的慢时间先验射频矩阵 F_RF
+disp('正在进行慢时间 (Slow-Time) 模拟射频网络硬件配置...');
+a_prior_broad = zeros(N_ant, 1);
+for theta_b = (theta_coarse_prior - window_half_width) : 0.5 : (theta_coarse_prior + window_half_width)
+    a_prior_broad = a_prior_broad + gen_a(theta_b, par);
+end
+a_prior_broad = a_prior_broad / norm(a_prior_broad);
+R_interference_prior = noise_power * eye(N_ant) + INR_linear * (a_clutter * a_clutter');
+w_digital_prior = inv(R_interference_prior) * a_prior_broad;
+w_digital_prior = w_digital_prior / norm(w_digital_prior);
+
+res_full = w_digital_prior; F_RF_omp_full_static = [];
+for k=1:N_RF
+    proj = A_dic_full' * res_full; [~, midx] = max(abs(proj));
+    F_RF_omp_full_static = [F_RF_omp_full_static, A_dic_full(:, midx)];
+    res_full = w_digital_prior - F_RF_omp_full_static * (pinv(F_RF_omp_full_static)*w_digital_prior);
+end
+res_tsc = w_digital_prior; F_RF_omp_static = [];
+for k=1:N_RF
+    proj = A_dic_constrained' * res_tsc; [~, midx] = max(abs(proj));
+    F_RF_omp_static = [F_RF_omp_static, A_dic_constrained(:, midx)];
+    res_tsc = w_digital_prior - F_RF_omp_static * (pinv(F_RF_omp_static)*w_digital_prior);
+end
+
+%% 4. 计算波束方向图并分别出图
+disp('正在计算并绘制静态波束方向图...');
+w_analog = a_target / sqrt(N_ant);
+sig_power_static = 10^(0/10); 
+s_t_static = sqrt(sig_power_static/2)*(randn(1,L_snapshots)+1j*randn(1,L_snapshots));
+s_c_static = sqrt(INR_linear/2)*(randn(1,L_snapshots)+1j*randn(1,L_snapshots));
+n_t_static = sqrt(noise_power/2)*(randn(N_ant,L_snapshots)+1j*randn(N_ant,L_snapshots));
+X_static = a_target*s_t_static + a_clutter*s_c_static + n_t_static; 
+R_in_static = (X_static*X_static')/L_snapshots;
+
+a_eff_dft = F_RF_dft'*a_target;
+R_eff_dft = F_RF_dft'*R_in_static*F_RF_dft; R_eff_dft = R_eff_dft + (0.05*trace(R_eff_dft)/N_RF)*eye(N_RF);
+F_BB_dft = (inv(R_eff_dft)*a_eff_dft)/(a_eff_dft'*inv(R_eff_dft)*a_eff_dft); w_hybrid_dft = F_RF_dft * F_BB_dft; w_hybrid_dft = w_hybrid_dft / norm(w_hybrid_dft);
+a_eff_full = F_RF_omp_full_static'*a_target;
+R_eff_full = F_RF_omp_full_static'*R_in_static*F_RF_omp_full_static; R_eff_full = R_eff_full + (0.05*trace(R_eff_full)/N_RF)*eye(N_RF);
+F_BB_full = (inv(R_eff_full)*a_eff_full)/(a_eff_full'*inv(R_eff_full)*a_eff_full); w_hybrid_omp_full = F_RF_omp_full_static * F_BB_full; w_hybrid_omp_full = w_hybrid_omp_full / norm(w_hybrid_omp_full);
+a_eff_tsc = F_RF_omp_static'*a_target;
+R_eff_tsc = F_RF_omp_static'*R_in_static*F_RF_omp_static; R_eff_tsc = R_eff_tsc + (0.05*trace(R_eff_tsc)/N_RF)*eye(N_RF);
+F_BB_tsc = (inv(R_eff_tsc)*a_eff_tsc)/(a_eff_tsc'*inv(R_eff_tsc)*a_eff_tsc); w_hybrid_omp = F_RF_omp_static * F_BB_tsc; w_hybrid_omp = w_hybrid_omp / norm(w_hybrid_omp);
+R_in_static_dl = R_in_static + (0.01*trace(R_in_static)/N_ant)*eye(N_ant);
+w_digital_ideal = (inv(R_in_static_dl)*a_target)/(a_target'*inv(R_in_static_dl)*a_target); w_digital_ideal = w_digital_ideal / norm(w_digital_ideal);
+
+BP_Analog = zeros(1,length(theta_scan)); BP_Digital = zeros(1,length(theta_scan)); BP_DFT = zeros(1,length(theta_scan)); BP_OMP_FULL = zeros(1,length(theta_scan)); BP_OMP = zeros(1,length(theta_scan));
+for p = 1:length(theta_scan)
+    a_scan = gen_a(theta_scan(p), par);
+    BP_Analog(p) = abs(a_scan'*w_analog)^2; BP_Digital(p) = abs(a_scan'*w_digital_ideal)^2;
+    BP_DFT(p) = abs(a_scan'*w_hybrid_dft)^2; BP_OMP_FULL(p) = abs(a_scan'*w_hybrid_omp_full)^2; BP_OMP(p) = abs(a_scan'*w_hybrid_omp)^2;
+end
+BP_Analog_dB = 10*log10(BP_Analog/max(BP_Analog)); BP_Digital_dB = 10*log10(BP_Digital/max(BP_Digital)); BP_DFT_dB = 10*log10(BP_DFT/max(BP_DFT)); BP_OMP_FULL_dB = 10*log10(BP_OMP_FULL/max(BP_OMP_FULL)); BP_OMP_dB = 10*log10(BP_OMP/max(BP_OMP));
+
+label_target = sprintf('机动目标 (%.2f°)', target_angle); label_clutter = sprintf('强杂波 (%.2f°)', clutter_angle);
+bp_labels = {'纯模拟架构', '纯数字架构(性能上限)', 'DFT混合架构', '传统OMP混合架构', 'TSC-OMP混合架构(所提)'};
+bp_data = {BP_Analog_dB, BP_Digital_dB, BP_DFT_dB, BP_OMP_FULL_dB, BP_OMP_dB}; bp_colors = {c_ana, c_dig, c_dft, c_omp, c_tsc};
+for fig_i = 1:5
+    figure('Name', ['波束图-', bp_labels{fig_i}], 'Position', [100+fig_i*20, 100+fig_i*20, 600, 400]);
+    plot(theta_scan, bp_data{fig_i}, 'Color', bp_colors{fig_i}, 'LineWidth', 2.5); hold on;
+    xline(target_angle,'g--','LineWidth',1.5,'Label',label_target,'LabelVerticalAlignment','top','LabelHorizontalAlignment','right','FontSize',11,'FontWeight','bold'); 
+    xline(clutter_angle,'r--','LineWidth',1.5,'Label',label_clutter,'LabelVerticalAlignment','top','LabelHorizontalAlignment','left','FontSize',11,'FontWeight','bold');
+    grid on; ylim([-60, 0]); xlim([-60, 60]); title(sprintf('波束方向图 - %s', bp_labels{fig_i})); xlabel('角度 (\circ)'); ylabel('归一化增益 (dB)'); legend('主波束', 'Location', 'southwest');
+end
+figure('Name', '波束图-五大架构汇总', 'Position', [250, 250, 700, 500]);
+plot(theta_scan, BP_Analog_dB, '-.', 'Color', c_ana, 'LineWidth', 2); hold on; plot(theta_scan, BP_Digital_dB, '-', 'Color', c_dig, 'LineWidth', 2); plot(theta_scan, BP_DFT_dB, '--', 'Color', c_dft, 'LineWidth', 2); plot(theta_scan, BP_OMP_FULL_dB, ':', 'Color', c_omp, 'LineWidth', 2.5); plot(theta_scan, BP_OMP_dB, '-', 'Color', c_tsc, 'LineWidth', 2.5);
+xline(target_angle,'g--','LineWidth',2,'Label',label_target,'LabelVerticalAlignment','top','LabelHorizontalAlignment','right','FontSize',11,'FontWeight','bold'); xline(clutter_angle,'r--','LineWidth',2,'Label',label_clutter,'LabelVerticalAlignment','top','LabelHorizontalAlignment','left','FontSize',11,'FontWeight','bold');
+grid on; ylim([-60, 0]); xlim([-60, 60]); title('五大架构波束方向图综合对比'); xlabel('角度 (\circ)'); ylabel('归一化增益 (dB)'); legend('纯模拟', '纯数字', 'DFT', '传统OMP', 'TSC-OMP(所提)', 'Location', 'southwest');
+
+%% 5. 理论 SINR 对比
+disp('正在计算 SINR...');
+SNR_dB_range = -10:2:20;
+SINR_Ana = zeros(1,length(SNR_dB_range)); SINR_Dig = zeros(1,length(SNR_dB_range)); SINR_DFT = zeros(1,length(SNR_dB_range)); SINR_OMP_FULL = zeros(1,length(SNR_dB_range)); SINR_OMP = zeros(1,length(SNR_dB_range));
+for i = 1:length(SNR_dB_range)
+    sig_power = 10^(SNR_dB_range(i)/10);
+    SINR_Ana(i) = (sig_power*abs(w_analog'*a_target)^2) / real(w_analog'*R_interference_prior*w_analog);
+    SINR_Dig(i) = (sig_power*abs(w_digital_ideal'*a_target)^2) / real(w_digital_ideal'*R_interference_prior*w_digital_ideal);
+    SINR_DFT(i) = (sig_power*abs(w_hybrid_dft'*a_target)^2) / real(w_hybrid_dft'*R_interference_prior*w_hybrid_dft);
+    SINR_OMP_FULL(i) = (sig_power*abs(w_hybrid_omp_full'*a_target)^2) / real(w_hybrid_omp_full'*R_interference_prior*w_hybrid_omp_full);
+    SINR_OMP(i) = (sig_power*abs(w_hybrid_omp'*a_target)^2) / real(w_hybrid_omp'*R_interference_prior*w_hybrid_omp);
+end
+figure('Name', 'SINR理论性能', 'Position', [300, 300, 600, 450]);
+plot(SNR_dB_range,10*log10(SINR_Ana),'-v','Color',c_ana,'LineWidth',2); hold on; plot(SNR_dB_range,10*log10(SINR_Dig),'-o','Color',c_dig,'LineWidth',2); plot(SNR_dB_range,10*log10(SINR_DFT),'-d','Color',c_dft,'LineWidth',2); plot(SNR_dB_range,10*log10(SINR_OMP_FULL),'-*','Color',c_omp,'LineWidth',2); plot(SNR_dB_range,10*log10(SINR_OMP),'-s','Color',c_tsc,'LineWidth',2);
+grid on; legend('纯模拟','纯数字(上限)','DFT','传统OMP','TSC-OMP(所提)','Location','northwest'); xlabel('输入 SNR (dB)'); ylabel('输出 SINR (dB)'); title(sprintf('全架构输出 SINR 理论逼近测试 (目标 %.2f°)', target_angle));
+
+%% 6. 【核心优化：开启多核并行计算 parfor】
+disp('正在调用多核并行引擎 (parfor) 加速蒙特卡洛评估...');
+Monte_Carlo = 500; 
+RMSE_Ana = zeros(1,length(SNR_dB_range)); RMSE_Dig = zeros(1,length(SNR_dB_range)); RMSE_DFT = zeros(1,length(SNR_dB_range)); RMSE_OMP_FULL = zeros(1,length(SNR_dB_range)); RMSE_OMP = zeros(1,length(SNR_dB_range));
+scan_grid = (theta_coarse_prior - window_half_width) : 0.01 : (theta_coarse_prior + window_half_width); 
+
+for i = 1:length(SNR_dB_range)
+    sig_power = 10^(SNR_dB_range(i)/10);
+    err_ana = zeros(1,Monte_Carlo); err_dig = zeros(1,Monte_Carlo); err_dft = zeros(1,Monte_Carlo); err_omp_full = zeros(1,Monte_Carlo); err_omp = zeros(1,Monte_Carlo);
+    
+    % 替换为 parfor，自动分配到 6 个 Worker 极速计算！
+    parfor mc = 1:Monte_Carlo
+        s_t = sqrt(sig_power/2)*(randn(1,L_snapshots)+1j*randn(1,L_snapshots));
+        s_c = sqrt(INR_linear/2)*(randn(1,L_snapshots)+1j*randn(1,L_snapshots));
+        n_t = sqrt(noise_power/2)*(randn(N_ant,L_snapshots)+1j*randn(N_ant,L_snapshots));
+        X_mc = a_target*s_t + a_clutter*s_c + n_t;
+        
+        R_in = (X_mc*X_mc')/L_snapshots;
+        inv_R_in_dl = inv(R_in + (0.05*trace(R_in)/N_ant)*eye(N_ant));
+        
+        Y_dft = F_RF_dft' * X_mc; Y_omp_full = F_RF_omp_full_static' * X_mc; Y_omp = F_RF_omp_static' * X_mc;
+        R_dft = (Y_dft*Y_dft')/L_snapshots; R_omp_full = (Y_omp_full*Y_omp_full')/L_snapshots; R_omp = (Y_omp*Y_omp')/L_snapshots;
+        
+        R_dft = R_dft + (0.05*trace(R_dft)/N_RF)*eye(N_RF); R_omp_full = R_omp_full + (0.05*trace(R_omp_full)/N_RF)*eye(N_RF); R_omp = R_omp + (0.05*trace(R_omp)/N_RF)*eye(N_RF);
+        inv_R_dft = inv(R_dft); inv_R_omp_full = inv(R_omp_full); inv_R_omp = inv(R_omp);
+        
+        P_ana = zeros(1,length(scan_grid)); P_dig = zeros(1,length(scan_grid)); P_dft = zeros(1,length(scan_grid)); P_omp_full = zeros(1,length(scan_grid)); P_omp = zeros(1,length(scan_grid));
+        
+        for a_idx = 1:length(scan_grid)
+            a_test = gen_a(scan_grid(a_idx), par);
+            P_ana(a_idx) = abs(a_test' * R_in * a_test); P_dig(a_idx) = 1/abs(a_test' * inv_R_in_dl * a_test);
+            P_dft(a_idx) = 1/abs((F_RF_dft'*a_test)'*inv_R_dft*(F_RF_dft'*a_test));
+            P_omp_full(a_idx) = 1/abs((F_RF_omp_full_static'*a_test)'*inv_R_omp_full*(F_RF_omp_full_static'*a_test));
+            P_omp(a_idx) = 1/abs((F_RF_omp_static'*a_test)'*inv_R_omp*(F_RF_omp_static'*a_test));
+        end
+        
+        [~,id1] = max(P_ana); [~,id2] = max(P_dig); [~,id3] = max(P_dft); [~,id4] = max(P_omp_full); [~,id5] = max(P_omp);
+        err_ana(mc) = (scan_grid(id1)-target_angle)^2; err_dig(mc) = (scan_grid(id2)-target_angle)^2;
+        err_dft(mc) = (scan_grid(id3)-target_angle)^2; err_omp_full(mc) = (scan_grid(id4)-target_angle)^2; err_omp(mc) = (scan_grid(id5)-target_angle)^2;
+    end
+    RMSE_Ana(i) = sqrt(mean(err_ana)); RMSE_Dig(i) = sqrt(mean(err_dig)); RMSE_DFT(i) = sqrt(mean(err_dft)); RMSE_OMP_FULL(i) = sqrt(mean(err_omp_full)); RMSE_OMP(i) = sqrt(mean(err_omp));
+end
+
+%% 7. 绘制分别与汇总的 RMSE 对比图
+rmse_data = {RMSE_Ana, RMSE_Dig, RMSE_DFT, RMSE_OMP_FULL, RMSE_OMP};
+for fig_i = 1:5
+    figure('Name', ['RMSE-', bp_labels{fig_i}], 'Position', [150+fig_i*20, 150+fig_i*20, 600, 400]);
+    semilogy(SNR_dB_range, rmse_data{fig_i}, '-o', 'Color', bp_colors{fig_i}, 'LineWidth', 2.5);
+    grid on; ylim([1e-3, 20]); yticks(10.^(-3:1:2)); title(sprintf('DOA 测角精度 - %s (目标 %.2f°)', bp_labels{fig_i}, target_angle)); xlabel('信噪比 SNR (dB)'); ylabel('测角 RMSE (\circ)');
+end
+figure('Name', 'RMSE-五大架构汇总', 'Position', [400, 400, 700, 500]);
+semilogy(SNR_dB_range, RMSE_Ana, '-.v', 'Color', c_ana, 'LineWidth', 2); hold on; semilogy(SNR_dB_range, RMSE_Dig, '-o', 'Color', c_dig, 'LineWidth', 2); semilogy(SNR_dB_range, RMSE_DFT, '--d', 'Color', c_dft, 'LineWidth', 2); semilogy(SNR_dB_range, RMSE_OMP_FULL, ':*', 'Color', c_omp, 'LineWidth', 2.5); semilogy(SNR_dB_range, RMSE_OMP, '-s', 'Color', c_tsc, 'LineWidth', 2.5);
+grid on; ylim([1e-3, 20]); yticks(10.^(-3:1:2)); title(sprintf('全架构实战恶劣环境 DOA 测角精度评估 (机动目标 %.2f°)', target_angle)); xlabel('信噪比 SNR (dB)'); ylabel('测角 RMSE (\circ)'); legend('纯模拟 (极低分辨率)','纯数字 (性能上限)','DFT','传统OMP (极易发散)','TSC-OMP (所提稳健算法)','Location','southwest');
+disp('并行测试完成！');

Graduation Design Matlab/setpar.m

@@ -0,0 +1,29 @@
+function [ par ] = setpar
+
+par.M = 24; % number of TX/RX antennas
+par.Nrf = 4; % number of RF chains
+par.Ns = 1; % number of data streams
+
+par.fc = 4.9e9; % system bandwidth
+
+par.Et = 1; % power
+par.Er = 1;
+%%----------------------------------ULAs-----------------------------------
+
+par.labmda = 3e8/par.fc;
+a = [0:43:11*43]';
+b = 11*43 + 63;
+c = 11*43 + 63 + [43:43:11*43]';
+par.delta_d = 1e-3*[a;b;c];
+
+phi = [-355, -240, -275, -160, -195, -80, -115, 0].';
+par.pos = exp(1j*phi);
+% PS network
+par.K1 = [1 1 1 1; 0 0 0 1; 1 1 1 1];
+par.K2 = [1 1; 0 1];
+
+%%--------------------discrete frequency space-----------------------------
+par.diff_p = 0.05; % total discreted points
+par.theta = round(-90:par.diff_p:90, 6);
+
+end