diff --git a/Code/Python/1.ULA/RMSE_Test_ula.py b/Code/Python/1.ULA/RMSE_Test_ula.py
new file mode 100644
index 0000000..9cb50ac
--- /dev/null
+++ b/Code/Python/1.ULA/RMSE_Test_ula.py
@@ -0,0 +1,146 @@
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+# ==========================================
+# 0. 全局绘图参数与 CUDA 引擎初始化
+# ==========================================
+plt.rcParams['axes.unicode_minus'] = False
+plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
+plt.rcParams['mathtext.fontset'] = 'stix' 
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+torch.manual_seed(2024)
+
+N_ant, N_RF, d_lambda = 24, 4, 0.5
+clutter_angle = -15.0
+noise_power = 1.0
+INR_linear = 10**(30/10)
+
+def gen_a(theta_deg):
+    theta_rad = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device))
+    n_idx = torch.arange(-(N_ant-1)/2, (N_ant-1)/2 + 0.1, device=device)
+    return torch.exp(1j * 2 * torch.pi * d_lambda * n_idx * torch.sin(theta_rad)).unsqueeze(1)
+
+a_clutter = gen_a(clutter_angle)
+R_interference = noise_power * torch.eye(N_ant, device=device) + INR_linear * (a_clutter @ a_clutter.mH)
+
+dic_angles_full = torch.arange(-90, 90.5, 0.5, device=device)
+A_dic_full = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_angles_full], dim=1)
+
+angle_ranges = [np.arange(0, 11), np.arange(11, 21), np.arange(21, 31), np.arange(31, 41)]
+snr_test_list = [0, 5, 10, 15, 20]
+Monte_Carlo = 100
+L_snapshots = 100
+colors = ['#0072BD', '#D95319', '#EDB120', '#7E2F8E', '#77AC30']
+
+def run_omp(w_opt, A_dic):
+    res = w_opt.clone()
+    F_RF_list = []
+    for k in range(N_RF):
+        idx = torch.argmax(torch.abs(A_dic.mH @ res))
+        F_RF_list.append(A_dic[:, idx:idx+1])
+        F_RF_mat = torch.cat(F_RF_list, dim=1)
+        res = w_opt - F_RF_mat @ (torch.linalg.pinv(F_RF_mat) @ w_opt)
+    return F_RF_mat
+
+print(f"启动 CUDA 并行引擎，对 4 大扇区进行极速扫雷测试 (MC={Monte_Carlo})...")
+start_time = time.time()
+
+# 取消了这里原本的 fig, axs = plt.subplots(2, 2)
+# 改为在循环内部动态创建单图
+
+for fig_idx, test_angles in enumerate(angle_ranges):
+    print(f"正在计算扇区 {test_angles[0]}° ~ {test_angles[-1]}° ...")
+    
+    # 【修改点 1】：为每一个扇区单独生成一张独立画布
+    fig, ax = plt.subplots(figsize=(9, 7))
+    
+    RMSE_DFT_All = np.zeros((len(snr_test_list), len(test_angles)))
+    RMSE_FULL_All = np.zeros((len(snr_test_list), len(test_angles)))
+    RMSE_TSC_All = np.zeros((len(snr_test_list), len(test_angles)))
+    
+    for snr_idx, snr_dB in enumerate(snr_test_list):
+        sig_p = 10**(snr_dB/10)
+        
+        for a_idx, t_angle in enumerate(test_angles):
+            true_target_angle = t_angle + 0.04
+            theta_prior = round(true_target_angle)
+            window = 5
+            
+            dic_tsc = torch.arange(theta_prior - window, theta_prior + window + 0.1, 0.1, device=device)
+            A_dic_tsc = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_tsc], dim=1)
+            
+            angles_dft = torch.linspace(theta_prior - window, theta_prior + window, N_RF, device=device)
+            F_RF_dft = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in angles_dft], dim=1)
+            
+            a_prior_broad = torch.zeros((N_ant, 1), dtype=torch.complex64, device=device)
+            for tb in torch.arange(theta_prior - window, theta_prior + window + 0.1, 0.5, device=device):
+                a_prior_broad += gen_a(tb)
+            a_prior_broad /= torch.norm(a_prior_broad)
+            
+            w_dig_prior = torch.linalg.inv(R_interference) @ a_prior_broad
+            w_dig_prior /= torch.norm(w_dig_prior)
+            
+            F_RF_full = run_omp(w_dig_prior, A_dic_full)
+            F_RF_tsc = run_omp(w_dig_prior, A_dic_tsc)
+            
+            scan_grid = torch.arange(theta_prior - window, theta_prior + window + 0.01, 0.01, device=device)
+            A_scan = torch.cat([gen_a(th) for th in scan_grid], dim=1)
+            
+            A_eff_dft = F_RF_dft.mH @ A_scan
+            A_eff_full = F_RF_full.mH @ A_scan
+            A_eff_tsc = F_RF_tsc.mH @ A_scan
+            
+            a_target = gen_a(true_target_angle)
+            s_t = np.sqrt(sig_p/2) * (torch.randn(Monte_Carlo, 1, L_snapshots, device=device) + 1j * torch.randn(Monte_Carlo, 1, L_snapshots, device=device))
+            s_c = np.sqrt(INR_linear/2) * (torch.randn(Monte_Carlo, 1, L_snapshots, device=device) + 1j * torch.randn(Monte_Carlo, 1, L_snapshots, device=device))
+            n_t = np.sqrt(noise_power/2) * (torch.randn(Monte_Carlo, N_ant, L_snapshots, device=device) + 1j * torch.randn(Monte_Carlo, N_ant, L_snapshots, device=device))
+            
+            X = a_target.unsqueeze(0) * s_t + a_clutter.unsqueeze(0) * s_c + n_t
+            
+            def process_hybrid(F_RF, A_eff):
+                Y = F_RF.mH.unsqueeze(0) @ X
+                R = (Y @ Y.mH) / L_snapshots
+                R += (0.05 * torch.diagonal(R, dim1=-2, dim2=-1).sum(-1).view(-1, 1, 1) / N_RF) * torch.eye(N_RF, device=device).unsqueeze(0)
+                inv_R = torch.linalg.inv(R)
+                P = 1.0 / torch.abs(torch.einsum('si, bij, js -> bs', A_eff.conj().T, inv_R, A_eff))
+                return np.sqrt(np.mean(((scan_grid[torch.argmax(P, dim=1)] - true_target_angle)**2).cpu().numpy()))
+
+            RMSE_DFT_All[snr_idx, a_idx] = process_hybrid(F_RF_dft, A_eff_dft)
+            RMSE_FULL_All[snr_idx, a_idx] = process_hybrid(F_RF_full, A_eff_full)
+            RMSE_TSC_All[snr_idx, a_idx] = process_hybrid(F_RF_tsc, A_eff_tsc)
+            
+        c_str = colors[snr_idx]
+        lbl_suffix = f" (SNR={snr_dB}dB)"
+        ax.semilogy(test_angles, RMSE_DFT_All[snr_idx, :], '--^', color=c_str, linewidth=1.5, label='DFT'+lbl_suffix)
+        ax.semilogy(test_angles, RMSE_FULL_All[snr_idx, :], ':x', color=c_str, linewidth=2, label='传统OMP'+lbl_suffix)
+        ax.semilogy(test_angles, RMSE_TSC_All[snr_idx, :], '-o', color=c_str, linewidth=2.5, label='TSC-OMP'+lbl_suffix)
+
+    ax.set_xlim([test_angles[0], test_angles[-1]])
+    ax.set_xticks(test_angles)
+    ax.grid(True, which="both", ls="--", alpha=0.5)
+    ax.set_ylim([1e-3, 20])
+    ax.set_xlabel('目标真实出现角度 $\\theta (\\circ)$', fontsize=12, fontweight='bold')
+    ax.set_ylabel(r'测角 RMSE $(^\circ)$', fontsize=12, fontweight='bold')
+    ax.set_title(f'宽波束先验引导下，扇区 ({test_angles[0]}°~{test_angles[-1]}°) 鲁棒性评估', fontsize=13, fontweight='bold')
+
+
+    # 【修改点 2】：独立设置每张图的图例。
+    plt.tight_layout() 
+    fig.subplots_adjust(bottom=0.25) 
+    
+    # 【关键调整】：将 bbox_to_anchor 的 Y 坐标从 -0.15 提上来，改为 -0.08 
+    # 这样图例就会离 X 轴更近，避开底部操作条
+    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.10), ncol=3, fontsize=10)
+
+    # 【修改点 3】：自动保存
+    file_name = f'RMSE_Sector_{test_angles[0]}_{test_angles[-1]}.png'
+    plt.savefig(file_name, dpi=300, bbox_inches='tight')
+    print(f"  已保存独立图表: {file_name}")
+
+print(f"全扇区计算完毕！总耗时: {time.time() - start_time:.4f} 秒。")
+
+# 一次性在屏幕上弹出生成的4张独立图表
+plt.show()
\ No newline at end of file
diff --git a/Code/Python/1.ULA/main_ula.py b/Code/Python/1.ULA/main_ula.py
new file mode 100644
index 0000000..6aebebb
--- /dev/null
+++ b/Code/Python/1.ULA/main_ula.py
@@ -0,0 +1,282 @@
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+# ==========================================
+# 0. 全局设置与 CUDA 引擎
+# ==========================================
+plt.rcParams['axes.unicode_minus'] = False
+plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 锁定随机数种子
+torch.manual_seed(2024)
+np.random.seed(2024)
+
+# 颜色设置
+c_ana = '#77AC30'
+c_dig = '#000000'
+c_dft = '#4DBEEE'
+c_omp = '#D95319'
+c_tsc = '#0072BD'
+
+# ==========================================
+# 1. 物理层参数初始化 (标准 ULA)
+# ==========================================
+N_ant, N_RF = 24, 4  #24根天线，4个射频链路
+#target_angle = float(input("请输入目标位置角度（°）（范围0-20°）："))   #目标位置
+#clutter_angle = float(input("请输入干扰位置角度（°）（范围-3°~-20°）："))  #干扰位置
+target_angle = 5.0
+clutter_angle = -15.0
+L_snapshots = 100
+noise_power = 1.0
+INR_linear = 10**(30/10)
+
+d_lambda = 0.5 # 经典的半波长间距
+
+def gen_a(theta_deg):
+    """标准的均匀线阵 (ULA) 导向矢量"""
+    theta_rad = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device))
+    # 以阵列中心为参考点
+    n_idx = torch.arange(-(N_ant-1)/2, (N_ant-1)/2 + 0.1, device=device)
+    return torch.exp(1j * 2 * torch.pi * d_lambda * n_idx * torch.sin(theta_rad)).unsqueeze(1)
+
+a_target = gen_a(target_angle)
+a_clutter = gen_a(clutter_angle)
+
+# ==========================================
+# 2. 动态生成目标约束扇区与字典
+# ==========================================
+theta_coarse_prior = round(target_angle)
+window_half_width = 5.0
+
+dic_angles_constrained = torch.arange(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width + 0.1, 0.1, device=device)
+A_dic_constrained = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_angles_constrained], dim=1)
+
+dic_angles_full = torch.arange(-90, 90.5, 0.5, device=device)
+A_dic_full = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_angles_full], dim=1)
+
+angles_dft = torch.linspace(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width, N_RF, device=device)
+F_RF_dft = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in angles_dft], dim=1)
+
+# ==========================================
+# 3. 提取防发散的慢时间先验射频矩阵 F_RF
+# ==========================================
+print('正在进行慢时间 (Slow-Time) 模拟射频网络硬件配置...')
+a_prior_broad = torch.zeros((N_ant, 1), dtype=torch.complex64, device=device)
+for theta_b in torch.arange(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width + 0.1, 0.5, device=device):
+    a_prior_broad += gen_a(theta_b)
+a_prior_broad /= torch.norm(a_prior_broad)
+
+R_interference_prior = noise_power * torch.eye(N_ant, device=device) + INR_linear * (a_clutter @ a_clutter.mH)
+w_digital_prior = torch.linalg.inv(R_interference_prior) @ a_prior_broad
+w_digital_prior /= torch.norm(w_digital_prior)
+
+def extract_omp(w_opt, A_dic):
+    res = w_opt.clone()
+    F_RF_mat = None
+    for k in range(N_RF):
+        proj = A_dic.mH @ res
+        idx = torch.argmax(torch.abs(proj))
+        new_atom = A_dic[:, idx:idx+1]
+        F_RF_mat = new_atom if F_RF_mat is None else torch.cat((F_RF_mat, new_atom), dim=1)
+        res = w_digital_prior - F_RF_mat @ (torch.linalg.pinv(F_RF_mat) @ w_digital_prior)
+    return F_RF_mat
+
+F_RF_omp_full_static = extract_omp(w_digital_prior, A_dic_full)
+F_RF_omp_static = extract_omp(w_digital_prior, A_dic_constrained)
+
+# ==========================================
+# 4. 计算波束方向图与理论 SINR
+# ==========================================
+print('正在计算静态波束方向图与理论 SINR...')
+w_analog = a_target / np.sqrt(N_ant)
+
+def calc_hybrid_weight(F_RF, R_in_stat):
+    a_eff = F_RF.mH @ a_target
+    R_eff = F_RF.mH @ R_in_stat @ F_RF
+    R_eff += (0.05 * torch.trace(R_eff).real / N_RF) * torch.eye(N_RF, device=device)
+    inv_R_eff = torch.linalg.inv(R_eff)
+    F_BB = (inv_R_eff @ a_eff) / (a_eff.mH @ inv_R_eff @ a_eff)
+    w_hyb = F_RF @ F_BB
+    return w_hyb / torch.norm(w_hyb)
+
+sig_power_static = 10**(0/10)
+# 直接使用无穷大快拍下的理论理想协方差矩阵，消除所有随机扰动，展示极限压制能力
+R_in_static = noise_power * torch.eye(N_ant, device=device) + \
+              sig_power_static * (a_target @ a_target.mH) + \
+              INR_linear * (a_clutter @ a_clutter.mH)
+
+w_hybrid_dft = calc_hybrid_weight(F_RF_dft, R_in_static)
+w_hybrid_omp_full = calc_hybrid_weight(F_RF_omp_full_static, R_in_static)
+w_hybrid_omp = calc_hybrid_weight(F_RF_omp_static, R_in_static)
+
+R_in_static_dl = R_in_static + (0.01 * torch.trace(R_in_static).real / N_ant) * torch.eye(N_ant, device=device)
+inv_R_stat = torch.linalg.inv(R_in_static_dl)
+w_digital_ideal = (inv_R_stat @ a_target) / (a_target.mH @ inv_R_stat @ a_target)
+w_digital_ideal /= torch.norm(w_digital_ideal)
+
+theta_scan = torch.arange(-90, 90.1, 0.1, device=device)
+A_scan_full = torch.cat([gen_a(th) for th in theta_scan], dim=1)
+
+def get_bp(w): return 10 * torch.log10((torch.abs(A_scan_full.mH @ w)**2).squeeze() / torch.max(torch.abs(A_scan_full.mH @ w)**2))
+BP_Ana = get_bp(w_analog).cpu().numpy()
+BP_Dig = get_bp(w_digital_ideal).cpu().numpy()
+BP_DFT = get_bp(w_hybrid_dft).cpu().numpy()
+BP_OMP_FULL = get_bp(w_hybrid_omp_full).cpu().numpy()
+BP_OMP = get_bp(w_hybrid_omp).cpu().numpy()
+theta_scan_np = theta_scan.cpu().numpy()
+
+SNR_dB_range = np.arange(-10, 22, 2)
+SINR_Ana, SINR_Dig, SINR_DFT, SINR_OMP_FULL, SINR_OMP = [], [], [], [], []
+for snr in SNR_dB_range:
+    sig_p = 10**(snr/10)
+    def calc_sinr(w): return (sig_p * torch.abs(w.mH @ a_target)**2) / torch.real(w.mH @ R_interference_prior @ w)
+    SINR_Ana.append(10 * torch.log10(calc_sinr(w_analog)).item())
+    SINR_Dig.append(10 * torch.log10(calc_sinr(w_digital_ideal)).item())
+    SINR_DFT.append(10 * torch.log10(calc_sinr(w_hybrid_dft)).item())
+    SINR_OMP_FULL.append(10 * torch.log10(calc_sinr(w_hybrid_omp_full)).item())
+    SINR_OMP.append(10 * torch.log10(calc_sinr(w_hybrid_omp)).item())
+
+# ==========================================
+# 5. 基于张量 Batch 操作的极致蒙特卡洛评估
+# ==========================================
+print('正在启动 CUDA 高维张量批量计算 (Batched Evaluation)...')
+Monte_Carlo = 500
+scan_grid = torch.arange(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width + 0.01, 0.01, device=device)
+A_scan = torch.cat([gen_a(th) for th in scan_grid], dim=1) 
+
+A_eff_dft = F_RF_dft.mH @ A_scan               
+A_eff_omp_full = F_RF_omp_full_static.mH @ A_scan
+A_eff_omp = F_RF_omp_static.mH @ A_scan
+
+RMSE_Ana, RMSE_Dig, RMSE_DFT, RMSE_OMP_FULL, RMSE_OMP = [], [], [], [], []
+start_time = time.time()
+
+for snr in SNR_dB_range:
+    sig_power = 10**(snr/10)
+    s_t = np.sqrt(sig_power/2) * (torch.randn(Monte_Carlo, 1, L_snapshots, device=device) + 1j*torch.randn(Monte_Carlo, 1, L_snapshots, device=device))
+    s_c = np.sqrt(INR_linear/2) * (torch.randn(Monte_Carlo, 1, L_snapshots, device=device) + 1j*torch.randn(Monte_Carlo, 1, L_snapshots, device=device))
+    n_t = np.sqrt(noise_power/2) * (torch.randn(Monte_Carlo, N_ant, L_snapshots, device=device) + 1j*torch.randn(Monte_Carlo, N_ant, L_snapshots, device=device))
+    
+    X_mc = a_target.unsqueeze(0) * s_t + a_clutter.unsqueeze(0) * s_c + n_t
+    R_in = torch.bmm(X_mc, X_mc.mH) / L_snapshots
+    trace_R_in = torch.diagonal(R_in, dim1=-2, dim2=-1).sum(-1).real
+    R_in_dl = R_in + (0.05 * trace_R_in / N_ant).view(-1, 1, 1) * torch.eye(N_ant, device=device).unsqueeze(0)
+    inv_R_in_dl = torch.linalg.inv(R_in_dl)
+    
+    P_ana = torch.einsum('si, bij, js -> bs', A_scan.conj().T, R_in, A_scan).abs()
+    P_dig = torch.einsum('si, bij, js -> bs', A_scan.conj().T, inv_R_in_dl, A_scan).abs()
+    
+    def process_hybrid_batched(F_RF, A_eff_hyb):
+        Y = F_RF.mH.unsqueeze(0) @ X_mc
+        R_hyb = torch.bmm(Y, Y.mH) / L_snapshots
+        trace_R = torch.diagonal(R_hyb, dim1=-2, dim2=-1).sum(-1).real
+        R_hyb += (0.05 * trace_R / N_RF).view(-1, 1, 1) * torch.eye(N_RF, device=device).unsqueeze(0)
+        inv_R_hyb = torch.linalg.inv(R_hyb)
+        P_hyb = torch.einsum('si, bij, js -> bs', A_eff_hyb.conj().T, inv_R_hyb, A_eff_hyb).abs()
+        return scan_grid[torch.argmin(P_hyb, dim=1)] 
+
+    ang_ana = scan_grid[torch.argmax(P_ana, dim=1)]
+    ang_dig = scan_grid[torch.argmin(P_dig, dim=1)]
+    ang_dft = process_hybrid_batched(F_RF_dft, A_eff_dft)
+    ang_omp_full = process_hybrid_batched(F_RF_omp_full_static, A_eff_omp_full)
+    ang_omp = process_hybrid_batched(F_RF_omp_static, A_eff_omp)
+
+    RMSE_Ana.append(torch.sqrt(torch.mean((ang_ana - target_angle)**2)).item())
+    RMSE_Dig.append(torch.sqrt(torch.mean((ang_dig - target_angle)**2)).item())
+    RMSE_DFT.append(torch.sqrt(torch.mean((ang_dft - target_angle)**2)).item())
+    RMSE_OMP_FULL.append(torch.sqrt(torch.mean((ang_omp_full - target_angle)**2)).item())
+    RMSE_OMP.append(torch.sqrt(torch.mean((ang_omp - target_angle)**2)).item())
+
+print(f"CUDA 并行测角完毕！耗时: {time.time() - start_time:.4f} 秒。")
+
+# ==========================================
+# 6. 绘图：单图输出与汇总输出
+# ==========================================
+bp_data = [BP_Ana, BP_Dig, BP_DFT, BP_OMP_FULL, BP_OMP]
+#bp_data = [BP_Ana, BP_Dig, BP_DFT, BP_OMP_FULL]
+bp_labels = ['纯模拟架构', '纯数字架构(MVDR)', 'DFT混合架构', '传统OMP混合架构', 'TSC-OMP混合架构']
+#bp_labels = ['纯模拟架构', '纯数字架构(MVDR)', 'DFT混合架构', '传统OMP混合架构']
+bp_colors = [c_ana, c_dig, c_dft, c_omp, c_tsc]
+bp_ls = ['-.', '-', '--', ':', '-']
+
+# 6.1 分别绘制 5 张独立的波束方向图
+for i in range(5):
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.plot(theta_scan_np, bp_data[i], color=bp_colors[i], linestyle=bp_ls[i], linewidth=2.5, label='主波束')
+    ax.axvline(x=target_angle, color='g', linestyle='--', linewidth=2, label=f'机动目标 ({target_angle}°)')
+    ax.axvline(x=clutter_angle, color='r', linestyle='--', linewidth=2, label=f'强杂波 ({clutter_angle}°)')
+    ax.set_ylim([-60, 0])
+    ax.set_xlim([-60, 60])
+    ax.grid(True, linestyle='--', alpha=0.6)
+    # 标题更新为 ULA
+    ax.set_title(f'波束方向图 (ULA) - {bp_labels[i]}', fontsize=14, fontweight='bold')
+    ax.set_xlabel(r'角度 $\theta (^\circ)$', fontsize=12)
+    ax.set_ylabel('归一化增益 (dB)', fontsize=12)
+    
+    plt.tight_layout()
+    fig.subplots_adjust(bottom=0.22)
+    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=3, fontsize=10)
+    
+    file_name = f'BP_Single_{i+1}_ULA_{bp_labels[i].split("(")[0]}.png'
+    plt.savefig(file_name, dpi=300, bbox_inches='tight')
+
+# 6.2 绘制五大架构综合对比波束图
+fig_all, ax_all = plt.subplots(figsize=(10, 6))
+for i in range(5):
+    ax_all.plot(theta_scan_np, bp_data[i], color=bp_colors[i], linestyle=bp_ls[i], linewidth=2.5, label=bp_labels[i].split("(")[0])
+ax_all.axvline(x=target_angle, color='g', linestyle='--', linewidth=2)
+ax_all.axvline(x=clutter_angle, color='r', linestyle='--', linewidth=2)
+ax_all.text(target_angle + 1, -5, f'机动目标 ({target_angle}°)', color='g', fontweight='bold')
+ax_all.text(clutter_angle - 1, -5, f'强杂波 ({clutter_angle}°)', color='r', fontweight='bold', ha='right')
+ax_all.set_ylim([-60, 0])
+ax_all.set_xlim([-60, 60])
+ax_all.grid(True, linestyle='--', alpha=0.6)
+ax_all.set_title('波束方向图综合对比 (ULA)', fontsize=14, fontweight='bold')
+ax_all.set_xlabel(r'角度 $\theta (^\circ)$', fontsize=12)
+ax_all.set_ylabel('归一化增益 (dB)', fontsize=12)
+
+plt.tight_layout()
+fig_all.subplots_adjust(bottom=0.22)
+ax_all.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=5, fontsize=10)
+plt.savefig('BP_All_Combined_ULA.png', dpi=300, bbox_inches='tight')
+
+# 6.3 绘制 SINR 对比图
+fig_sinr, ax_sinr = plt.subplots(figsize=(10, 6))
+ax_sinr.plot(SNR_dB_range, SINR_Ana, '-v', color=c_ana, linewidth=2, label='纯模拟')
+ax_sinr.plot(SNR_dB_range, SINR_Dig, '-o', color=c_dig, linewidth=2, label='纯数字(上限)')
+ax_sinr.plot(SNR_dB_range, SINR_DFT, '-d', color=c_dft, linewidth=2, label='DFT')
+ax_sinr.plot(SNR_dB_range, SINR_OMP_FULL, '-*', color=c_omp, linewidth=2, label='传统OMP')
+ax_sinr.plot(SNR_dB_range, SINR_OMP, '-s', color=c_tsc, linewidth=2.5, label='TSC-OMP')
+ax_sinr.grid(True, linestyle='--', alpha=0.6)
+ax_sinr.set_title(f'全架构输出 SINR 理论逼近测试 (ULA - 目标 {target_angle}°)', fontsize=14, fontweight='bold')
+ax_sinr.set_xlabel('输入 SNR (dB)', fontsize=12)
+ax_sinr.set_ylabel('输出 SINR (dB)', fontsize=12)
+
+plt.tight_layout()
+fig_sinr.subplots_adjust(bottom=0.22)
+ax_sinr.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=5, fontsize=10)
+plt.savefig('SINR_All_Combined_ULA.png', dpi=300, bbox_inches='tight')
+
+# 6.4 绘制 RMSE 对比图
+fig_rmse, ax_rmse = plt.subplots(figsize=(10, 6))
+ax_rmse.semilogy(SNR_dB_range, RMSE_Ana, '-.v', color=c_ana, linewidth=2, label='纯模拟')
+ax_rmse.semilogy(SNR_dB_range, RMSE_Dig, '-o', color=c_dig, linewidth=2, label='纯数字(上限)')
+ax_rmse.semilogy(SNR_dB_range, RMSE_DFT, '--d', color=c_dft, linewidth=2, label='DFT')
+ax_rmse.semilogy(SNR_dB_range, RMSE_OMP_FULL, ':*', color=c_omp, linewidth=2.5, label='传统OMP')
+ax_rmse.semilogy(SNR_dB_range, RMSE_OMP, '-s', color=c_tsc, linewidth=2.5, label='TSC-OMP')
+ax_rmse.grid(True, which="both", ls="--", alpha=0.6)
+ax_rmse.set_ylim([1e-3, 20])
+ax_rmse.set_title(f'全架构实战恶劣环境 DOA 测角精度评估 (ULA)', fontsize=14, fontweight='bold')
+ax_rmse.set_xlabel('信噪比 SNR (dB)', fontsize=12)
+ax_rmse.set_ylabel(r'测角 RMSE $(^\circ)$', fontsize=12)
+
+plt.tight_layout()
+fig_rmse.subplots_adjust(bottom=0.22)
+ax_rmse.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=5, fontsize=10)
+plt.savefig('RMSE_All_Combined_ULA.png', dpi=300, bbox_inches='tight')
+
+plt.show()
+print('计算完毕，单图与综合图表均已独立生成。')
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