上传文件至「Graduation Design Matlab」

Graduation Design Matlab/algo_digital_mvdr.m

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+function [w_mvdr] = algo_digital_mvdr(a_target, R_in)
+% algo_digital_mvdr: 基于最小方差无失真响应的纯数字波束成形
+% 输入:
+%   a_target: 目标方向的导向矢量
+%   R_in:     干扰加噪声协方差矩阵
+% 输出:
+%   w_mvdr:   最优数字波束权重
+
+    % 求协方差矩阵的逆
+    R_inv = inv(R_in);
+    
+    % MVDR 核心公式
+    numerator = R_inv * a_target;
+    denominator = a_target' * R_inv * a_target;
+    w_mvdr = numerator / denominator;
+    
+    % 功率归一化
+    w_mvdr = w_mvdr / norm(w_mvdr);
+end

Graduation Design Matlab/algo_hybrid_dft.m

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+function [w_hybrid, F_RF] = algo_hybrid_dft(w_opt, A_dic, N_RF, a_target, R_in)
+% algo_hybrid_dft: 基于最大相关性(DFT)选择 + 数字基带 MVDR 的基线算法
+    
+    % 1. 找出与理想波束相关性最大的 N_RF 个原子
+    projection = A_dic' * w_opt;
+    [~, sort_idx] = sort(abs(projection), 'descend');
+    F_RF = A_dic(:, sort_idx(1:N_RF));
+    
+    % 2. 同样使用数字基带 MVDR 压制干扰
+    a_eff = F_RF' * a_target;
+    R_eff = F_RF' * R_in * F_RF;
+    F_BB_mvdr = (inv(R_eff) * a_eff) / (a_eff' * inv(R_eff) * a_eff);
+    
+    % 3. 合并
+    w_hybrid = F_RF * F_BB_mvdr;
+    w_hybrid = w_hybrid / norm(w_hybrid);
+end
+

Graduation Design Matlab/algo_hybrid_omp.m

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+function [w_hybrid, F_RF] = algo_hybrid_omp(w_opt, A_dic, N_RF, a_target, R_in)
+% algo_hybrid_omp: 基于 OMP 模拟选择 + 数字基带 MVDR 抗干扰的混合波束成形
+% 核心改进：引入等效信道与基带抗干扰机制
+
+    % --- 阶段 1：OMP 寻找最优模拟预编码矩阵 F_RF ---
+    residual = w_opt;
+    F_RF = [];
+    for k = 1:N_RF
+        projection = A_dic' * residual;
+        [~, max_idx] = max(abs(projection));
+        F_RF = [F_RF, A_dic(:, max_idx)];
+        % 临时最小二乘用于更新 OMP 残差
+        F_BB_temp = pinv(F_RF) * w_opt;
+        residual = w_opt - F_RF * F_BB_temp;
+    end
+    
+    % --- 阶段 2：大招！数字基带 MVDR 深度抗干扰 ---
+    % 计算通过模拟网络后的 "等效导向矢量" (4x1)
+    a_eff = F_RF' * a_target;
+    % 计算通过模拟网络后的 "等效干扰加噪声协方差矩阵" (4x4)
+    R_eff = F_RF' * R_in * F_RF;
+    
+    % 在 4 维数字域计算最优基带权重，强行压制漏入的干扰
+    F_BB_mvdr = (inv(R_eff) * a_eff) / (a_eff' * inv(R_eff) * a_eff);
+    
+    % 最终混合权重
+    w_hybrid = F_RF * F_BB_mvdr;
+    w_hybrid = w_hybrid / norm(w_hybrid); % 归一化
+end

Graduation Design Matlab/algo_hybrid_robust_omp.m

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+function [w_hybrid, F_RF] = algo_hybrid_robust_omp(w_opt, A_dic, N_RF, a_target, R)
+
+% 输入：
+% w_opt：理想数字波束（MVDR结果）
+% A_dic：字典
+% N_RF：RF链数
+
+[N, ~] = size(A_dic);
+
+residual = w_opt;          % 初始残差
+selected_index = [];       % 已选原子
+lambda = 0.01;             % 正则化参数（关键）
+
+max_iter = N_RF;
+
+for iter = 1:max_iter
+
+    % =========================
+    % Step1：相关性计算
+    % =========================
+    corr = abs(A_dic' * residual);
+
+    % 找Top-2（而不是Top-1）
+    [~, idx_sorted] = sort(corr, 'descend');
+    new_idx = idx_sorted(1:2);
+
+    % 合并索引（避免重复）
+    selected_index = unique([selected_index new_idx']);
+
+    % =========================
+    % Step2：构造子字典
+    % =========================
+    A_selected = A_dic(:, selected_index);
+
+    % =========================
+    % Step3：正则化最小二乘（核心改进）
+    % =========================
+    x = (A_selected' * A_selected + lambda * eye(length(selected_index))) \ ...
+        (A_selected' * w_opt);
+
+    % =========================
+    % Step4：更新残差
+    % =========================
+    residual = w_opt - A_selected * x;
+
+    % =========================
+    % Step5：提前停止（关键！）
+    % =========================
+    if norm(residual) < 1e-3
+        break;
+    end
+
+end
+
+% =========================
+% 构造RF矩阵
+% =========================
+F_RF = A_dic(:, selected_index);
+
+% 模拟约束（只保留相位）
+F_RF = exp(1j * angle(F_RF));
+
+% =========================
+% 数字基带优化
+% =========================
+F_BB = pinv(F_RF) * w_opt;
+
+% 最终波束
+w_hybrid = F_RF * F_BB;
+
+% 归一化
+w_hybrid = w_hybrid / norm(w_hybrid);
+
+end

Graduation Design Matlab/gen_a.m

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+function [a] = gen_a(theta, par)
+
+a = exp(-1j*2*pi*par.delta_d*sind(theta)/par.labmda);
+
+end