diff --git a/Graduation Design/Complexity_Test.py b/Graduation Design/Complexity_Test.py
new file mode 100644
index 0000000..53facee
--- /dev/null
+++ b/Graduation Design/Complexity_Test.py	
@@ -0,0 +1,641 @@
+import os
+import time
+import math
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+
+# =========================================================
+# 0. 全局设置
+# =========================================================
+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)
+np.random.seed(2024)
+
+print(f"使用设备: {device}")
+
+SAVE_DIR = "analysis_results_ula_nula"
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+ARRAY_TYPES = ["ULA", "NULA"]
+
+# =========================================================
+# 1. 基本参数
+# =========================================================
+N_ant_default = 24
+N_RF_default = 4
+target_angle_default = 5.0
+clutter_angle_default = -15.0
+noise_power = 1.0
+INR_linear = 10 ** (30 / 10)
+L_snapshots = 100
+
+# 分析配置
+SNR_dB_test = 10
+Monte_Carlo_test = 80
+SCAN_STEP = 0.02
+
+# 颜色
+method_colors = {
+    "Analog": "#77AC30",
+    "Digital": "#000000",
+    "DFT": "#4DBEEE",
+    "OMP": "#D95319",
+    "TSC-OMP": "#0072BD",
+}
+array_colors = {
+    "ULA": "#7E2F8E",
+    "NULA": "#EDB120",
+}
+
+# =========================================================
+# 2. 阵列几何
+# =========================================================
+def get_positions_lambda(array_type: str, N_ant: int):
+    """
+    返回阵列位置，单位统一为“波长 λ”
+    - ULA: 半波长均匀线阵
+    - NULA:
+        * N_ant == 24 时，严格复刻你现有主线脚本中的非均匀阵列物理位置
+        * 否则使用一个可复现的广义非均匀阵列，用于复杂度规模扫描
+    """
+    array_type = array_type.upper()
+
+    if array_type == "ULA":
+        pos = 0.5 * np.arange(-(N_ant - 1) / 2, (N_ant - 1) / 2 + 1)
+        return pos.astype(np.float32)
+
+    # 精确复刻你当前 NULA 主线
+    if array_type == "NULA" and N_ant == 24:
+        fc = 4.9e9
+        lam = 3e8 / fc
+        a = np.arange(0, 11 * 43 + 1, 43)
+        b = np.array([11 * 43 + 63])
+        c = 11 * 43 + 63 + np.arange(43, 11 * 43 + 1, 43)
+        delta_d = 1e-3 * np.concatenate((a, b, c))  # meter
+        pos = delta_d / lam
+        return pos.astype(np.float32)
+
+    # 广义 NULA，用于 N_ant != 24 的复杂度缩放分析
+    base = 0.5 * np.arange(N_ant)
+    jitter = 0.08 * np.sin(np.linspace(0, 3 * np.pi, N_ant))
+    pos = base + jitter
+    pos = pos - np.mean(pos)
+    return pos.astype(np.float32)
+
+
+def gen_a(theta_deg, N_ant, array_type="NULA"):
+    """
+    导向矢量，统一使用 exp(-j 2π p sinθ)
+    p 为以 λ 为单位的阵元位置
+    """
+    pos = get_positions_lambda(array_type, N_ant)
+    pos_t = torch.tensor(pos, dtype=torch.float32, device=device).unsqueeze(1)
+    theta_rad = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device))
+    return torch.exp(-1j * 2 * torch.pi * pos_t * torch.sin(theta_rad))
+
+
+def build_dictionary(angle_grid, N_ant, array_type="NULA"):
+    return torch.cat(
+        [gen_a(float(th), N_ant, array_type) / math.sqrt(N_ant) for th in angle_grid],
+        dim=1
+    )
+
+
+def sync_if_needed():
+    if device.type == "cuda":
+        torch.cuda.synchronize()
+
+
+# =========================================================
+# 3. 五种架构模块
+# =========================================================
+def extract_omp(w_opt, A_dic, N_RF):
+    """
+    传统 OMP / TSC-OMP 公用提取函数
+    """
+    res = w_opt.clone()
+    F_RF = None
+    for _ in range(N_RF):
+        proj = A_dic.mH @ res
+        idx = torch.argmax(torch.abs(proj))
+        atom = A_dic[:, idx:idx+1]
+        F_RF = atom if F_RF is None else torch.cat((F_RF, atom), dim=1)
+        res = w_opt - F_RF @ (torch.linalg.pinv(F_RF) @ w_opt)
+    return F_RF
+
+
+def build_dft_rf(theta_prior, window_half_width, N_ant, N_RF, array_type="NULA"):
+    angles_dft = torch.linspace(
+        theta_prior - window_half_width,
+        theta_prior + window_half_width,
+        N_RF,
+        device=device
+    )
+    return torch.cat(
+        [gen_a(float(th), N_ant, array_type) / math.sqrt(N_ant) for th in angles_dft],
+        dim=1
+    )
+
+
+def make_prior_broad(theta_prior, window_half_width, N_ant, array_type="NULA"):
+    a_prior_broad = torch.zeros((N_ant, 1), dtype=torch.complex64, device=device)
+    for tb in torch.arange(
+        theta_prior - window_half_width,
+        theta_prior + window_half_width + 0.1,
+        0.5,
+        device=device
+    ):
+        a_prior_broad += gen_a(float(tb), N_ant, array_type)
+    a_prior_broad /= torch.norm(a_prior_broad)
+    return a_prior_broad
+
+
+def calc_hybrid_weight(F_RF, a_target, R_in, N_RF):
+    a_eff = F_RF.mH @ a_target
+    R_eff = F_RF.mH @ R_in @ F_RF
+    R_eff = 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 = F_RF @ F_BB
+    return w / torch.norm(w)
+
+
+def build_all_structures(true_target_angle, clutter_angle, N_ant, N_RF,
+                         theta_prior, window_half_width, array_type="NULA"):
+    """
+    构建五种架构所需的静态权值/射频矩阵
+    """
+    a_target = gen_a(true_target_angle, N_ant, array_type)
+    a_clutter = gen_a(clutter_angle, N_ant, array_type)
+
+    R_interf = noise_power * torch.eye(N_ant, device=device) + INR_linear * (a_clutter @ a_clutter.mH)
+
+    # 用于 OMP / TSC-OMP 的先验宽波束
+    a_prior_broad = make_prior_broad(theta_prior, window_half_width, N_ant, array_type)
+    w_digital_prior = torch.linalg.inv(R_interf) @ a_prior_broad
+    w_digital_prior /= torch.norm(w_digital_prior)
+
+    full_grid = np.arange(-90, 90.5, 0.5)
+    tsc_grid = np.arange(
+        theta_prior - window_half_width,
+        theta_prior + window_half_width + 0.1,
+        0.1
+    )
+
+    A_dic_full = build_dictionary(full_grid, N_ant, array_type)
+    A_dic_tsc = build_dictionary(tsc_grid, N_ant, array_type)
+
+    F_RF_dft = build_dft_rf(theta_prior, window_half_width, N_ant, N_RF, array_type)
+    F_RF_omp = extract_omp(w_digital_prior, A_dic_full, N_RF)
+    F_RF_tsc = extract_omp(w_digital_prior, A_dic_tsc, N_RF)
+
+    # 静态协方差，用于纯数字与混合权值展示
+    sig_power_static = 10 ** (0 / 10)
+    R_static = (noise_power * torch.eye(N_ant, device=device)
+                + sig_power_static * (a_target @ a_target.mH)
+                + INR_linear * (a_clutter @ a_clutter.mH))
+    R_static_dl = R_static + (0.01 * torch.trace(R_static).real / N_ant) * torch.eye(N_ant, device=device)
+
+    # 纯模拟
+    w_analog = a_target / torch.norm(a_target)
+
+    # 纯数字 MVDR
+    inv_R = torch.linalg.inv(R_static_dl)
+    w_digital = (inv_R @ a_target) / (a_target.mH @ inv_R @ a_target)
+    w_digital = w_digital / torch.norm(w_digital)
+
+    return {
+        "a_target": a_target,
+        "a_clutter": a_clutter,
+        "w_analog": w_analog,
+        "w_digital": w_digital,
+        "F_RF_dft": F_RF_dft,
+        "F_RF_omp": F_RF_omp,
+        "F_RF_tsc": F_RF_tsc,
+    }
+
+
+# =========================================================
+# 4. 统一 RMSE 评估
+# =========================================================
+def estimate_rmse_all_methods(true_target_angle, clutter_angle, N_ant, N_RF,
+                              snr_db, theta_prior, window_half_width,
+                              array_type="NULA", mc=80, scan_step=0.02):
+    """
+    返回:
+    analog_rmse, digital_rmse, dft_rmse, omp_rmse, tsc_rmse
+    """
+    sig_power = 10 ** (snr_db / 10)
+
+    structures = build_all_structures(
+        true_target_angle, clutter_angle, N_ant, N_RF,
+        theta_prior, window_half_width, array_type
+    )
+
+    a_target = structures["a_target"]
+    a_clutter = structures["a_clutter"]
+    w_analog = structures["w_analog"]
+    w_digital = structures["w_digital"]
+    F_RF_dft = structures["F_RF_dft"]
+    F_RF_omp = structures["F_RF_omp"]
+    F_RF_tsc = structures["F_RF_tsc"]
+
+    scan_grid = torch.arange(
+        theta_prior - window_half_width,
+        theta_prior + window_half_width + 0.001,
+        scan_step,
+        device=device
+    )
+    A_scan = torch.cat([gen_a(float(th), N_ant, array_type) for th in scan_grid], dim=1)
+
+    s_t = np.sqrt(sig_power / 2) * (
+        torch.randn(mc, 1, L_snapshots, device=device) +
+        1j * torch.randn(mc, 1, L_snapshots, device=device)
+    )
+    s_c = np.sqrt(INR_linear / 2) * (
+        torch.randn(mc, 1, L_snapshots, device=device) +
+        1j * torch.randn(mc, 1, L_snapshots, device=device)
+    )
+    n_t = np.sqrt(noise_power / 2) * (
+        torch.randn(mc, N_ant, L_snapshots, device=device) +
+        1j * torch.randn(mc, N_ant, L_snapshots, device=device)
+    )
+    X = a_target.unsqueeze(0) * s_t + a_clutter.unsqueeze(0) * s_c + n_t
+
+    # -------- 纯模拟：Bartlett --------
+    P_ana = torch.abs(torch.einsum('si,bij,js->bs', A_scan.conj().T, torch.bmm(X, X.mH) / L_snapshots, A_scan))
+    ang_ana = scan_grid[torch.argmax(P_ana, dim=1)]
+
+    # -------- 纯数字 MVDR：Capon --------
+    R_in = torch.bmm(X, X.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 = torch.linalg.inv(R_in_dl)
+    P_dig = 1.0 / torch.abs(torch.einsum('si,bij,js->bs', A_scan.conj().T, inv_R_in, A_scan))
+    ang_dig = scan_grid[torch.argmax(P_dig, dim=1)]
+
+    # -------- 混合三种 --------
+    def process_hybrid(F_RF):
+        A_eff = F_RF.mH @ A_scan
+        Y = F_RF.mH.unsqueeze(0) @ X
+        R_hyb = torch.bmm(Y, Y.mH) / L_snapshots
+        trace_R = torch.diagonal(R_hyb, dim1=-2, dim2=-1).sum(-1).real
+        R_hyb = 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 = 1.0 / torch.abs(torch.einsum('si,bij,js->bs', A_eff.conj().T, inv_R_hyb, A_eff))
+        return scan_grid[torch.argmax(P, dim=1)]
+
+    ang_dft = process_hybrid(F_RF_dft)
+    ang_omp = process_hybrid(F_RF_omp)
+    ang_tsc = process_hybrid(F_RF_tsc)
+
+    def rmse(x):
+        return torch.sqrt(torch.mean((x - true_target_angle) ** 2)).item()
+
+    return rmse(ang_ana), rmse(ang_dig), rmse(ang_dft), rmse(ang_omp), rmse(ang_tsc)
+
+
+# =========================================================
+# 5. 复杂度分析
+# =========================================================
+def complexity_proxy(N_ant, N_RF, D_full, D_tsc):
+    """
+    五种架构的复杂度代理量
+    """
+    c_ana = N_ant
+    c_dig = N_ant ** 3
+    c_dft = N_ant * N_RF + N_RF ** 3
+    c_omp = N_RF * N_ant * D_full + N_RF ** 4
+    c_tsc = N_RF * N_ant * D_tsc + N_RF ** 4
+    return c_ana, c_dig, c_dft, c_omp, c_tsc
+
+
+def run_complexity_analysis():
+    print("\n========== 开始复杂度分析（ULA + NULA） ==========")
+
+    target_angle = target_angle_default
+    clutter_angle = clutter_angle_default
+    N_RF = N_RF_default
+    window_half_width = 5.0
+    repeat_times = 20
+
+    N_ant_list = [8, 12, 16, 20, 24, 28, 32]
+    results_runtime = {}
+    results_proxy = {}
+
+    for array_type in ARRAY_TYPES:
+        print(f"\n--- 复杂度分析: {array_type} ---")
+        t_ana, t_dig, t_dft, t_omp, t_tsc = [], [], [], [], []
+        c_ana_list, c_dig_list, c_dft_list, c_omp_list, c_tsc_list = [], [], [], [], []
+
+        for N_ant in N_ant_list:
+            a_target = gen_a(target_angle, N_ant, array_type)
+            a_clutter = gen_a(clutter_angle, N_ant, array_type)
+
+            theta_prior = round(target_angle)
+            full_grid = np.arange(-90, 90.5, 0.5)
+            tsc_grid = np.arange(theta_prior - window_half_width,
+                                 theta_prior + window_half_width + 0.1,
+                                 0.1)
+
+            A_dic_full = build_dictionary(full_grid, N_ant, array_type)
+            A_dic_tsc = build_dictionary(tsc_grid, N_ant, array_type)
+
+            R_interf = noise_power * torch.eye(N_ant, device=device) + INR_linear * (a_clutter @ a_clutter.mH)
+            a_prior_broad = make_prior_broad(theta_prior, window_half_width, N_ant, array_type)
+
+            # 纯模拟
+            sync_if_needed()
+            tic = time.perf_counter()
+            for _ in range(repeat_times):
+                _ = a_target / torch.norm(a_target)
+            sync_if_needed()
+            t_ana.append((time.perf_counter() - tic) / repeat_times * 1e3)
+
+            # 纯数字 MVDR
+            sync_if_needed()
+            tic = time.perf_counter()
+            for _ in range(repeat_times):
+                R_static = (noise_power * torch.eye(N_ant, device=device)
+                            + (a_target @ a_target.mH)
+                            + INR_linear * (a_clutter @ a_clutter.mH))
+                R_static_dl = R_static + (0.01 * torch.trace(R_static).real / N_ant) * torch.eye(N_ant, device=device)
+                inv_R = torch.linalg.inv(R_static_dl)
+                _ = (inv_R @ a_target) / (a_target.mH @ inv_R @ a_target)
+            sync_if_needed()
+            t_dig.append((time.perf_counter() - tic) / repeat_times * 1e3)
+
+            # DFT
+            sync_if_needed()
+            tic = time.perf_counter()
+            for _ in range(repeat_times):
+                F_RF_dft = build_dft_rf(theta_prior, window_half_width, N_ant, N_RF, array_type)
+                R_static = (noise_power * torch.eye(N_ant, device=device)
+                            + (a_target @ a_target.mH)
+                            + INR_linear * (a_clutter @ a_clutter.mH))
+                _ = calc_hybrid_weight(F_RF_dft, a_target, R_static, N_RF)
+            sync_if_needed()
+            t_dft.append((time.perf_counter() - tic) / repeat_times * 1e3)
+
+            # OMP/TSC
+            w_digital_prior = torch.linalg.inv(R_interf) @ a_prior_broad
+            w_digital_prior /= torch.norm(w_digital_prior)
+
+            sync_if_needed()
+            tic = time.perf_counter()
+            for _ in range(repeat_times):
+                _ = extract_omp(w_digital_prior, A_dic_full, N_RF)
+            sync_if_needed()
+            t_omp.append((time.perf_counter() - tic) / repeat_times * 1e3)
+
+            sync_if_needed()
+            tic = time.perf_counter()
+            for _ in range(repeat_times):
+                _ = extract_omp(w_digital_prior, A_dic_tsc, N_RF)
+            sync_if_needed()
+            t_tsc.append((time.perf_counter() - tic) / repeat_times * 1e3)
+
+            c_ana, c_dig, c_dft, c_omp, c_tsc = complexity_proxy(
+                N_ant, N_RF, len(full_grid), len(tsc_grid)
+            )
+            c_ana_list.append(c_ana)
+            c_dig_list.append(c_dig)
+            c_dft_list.append(c_dft)
+            c_omp_list.append(c_omp)
+            c_tsc_list.append(c_tsc)
+
+        results_runtime[array_type] = {
+            "N_ant": N_ant_list,
+            "Analog": t_ana,
+            "Digital": t_dig,
+            "DFT": t_dft,
+            "OMP": t_omp,
+            "TSC-OMP": t_tsc,
+        }
+        results_proxy[array_type] = {
+            "N_ant": N_ant_list,
+            "Analog": c_ana_list,
+            "Digital": c_dig_list,
+            "DFT": c_dft_list,
+            "OMP": c_omp_list,
+            "TSC-OMP": c_tsc_list,
+        }
+
+        # 每种阵列单独出图
+        plt.figure(figsize=(10, 6))
+        for method in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]:
+            plt.plot(N_ant_list, results_runtime[array_type][method], linewidth=2, label=method, color=method_colors[method])
+        plt.grid(True, ls='--', alpha=0.6)
+        plt.xlabel('阵元数 N_ant')
+        plt.ylabel('平均运行时间 (ms)')
+        plt.title(f'复杂度分析：运行时间随阵元数变化 ({array_type})')
+        plt.legend()
+        plt.tight_layout()
+        plt.savefig(os.path.join(SAVE_DIR, f'complexity_runtime_vs_ant_{array_type}.png'), dpi=300)
+        plt.show()
+
+        plt.figure(figsize=(10, 6))
+        for method in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]:
+            plt.plot(N_ant_list, results_proxy[array_type][method], linewidth=2, label=method, color=method_colors[method])
+        plt.grid(True, ls='--', alpha=0.6)
+        plt.xlabel('阵元数 N_ant')
+        plt.ylabel('复杂度代理量')
+        plt.title(f'复杂度分析：复杂度代理量随阵元数变化 ({array_type})')
+        plt.legend()
+        plt.tight_layout()
+        plt.savefig(os.path.join(SAVE_DIR, f'complexity_proxy_vs_ant_{array_type}.png'), dpi=300)
+        plt.show()
+
+    # ULA vs NULA 对照图（固定 24 阵元）
+    idx_24 = N_ant_list.index(24)
+    methods = ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]
+    ula_vals = [results_runtime["ULA"][m][idx_24] for m in methods]
+    nula_vals = [results_runtime["NULA"][m][idx_24] for m in methods]
+
+    x = np.arange(len(methods))
+    width = 0.35
+
+    plt.figure(figsize=(10, 6))
+    plt.bar(x - width/2, ula_vals, width, label='ULA', color=array_colors["ULA"])
+    plt.bar(x + width/2, nula_vals, width, label='NULA', color=array_colors["NULA"])
+    plt.xticks(x, methods)
+    plt.ylabel('平均运行时间 (ms)')
+    plt.title('复杂度分析：24阵元下 ULA 与 NULA 运行时间对比')
+    plt.grid(True, axis='y', ls='--', alpha=0.5)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(os.path.join(SAVE_DIR, 'complexity_runtime_compare_ULA_NULA_24ant.png'), dpi=300)
+    plt.show()
+
+    print("复杂度分析完成。")
+    return results_runtime, results_proxy
+
+
+# =========================================================
+# 6. 局限性分析
+# =========================================================
+def run_limitation_analysis():
+    print("\n========== 开始局限性分析（ULA + NULA） ==========")
+
+    N_ant = N_ant_default
+    N_RF = N_RF_default
+    true_target_angle = target_angle_default
+    clutter_angle = clutter_angle_default
+    snr_db = SNR_dB_test
+
+    limitation_results = {}
+
+    for array_type in ARRAY_TYPES:
+        print(f"\n--- 局限性分析: {array_type} ---")
+        limitation_results[array_type] = {}
+
+        # 6.1 先验误差敏感性
+        prior_errors = np.arange(-8, 9, 1)
+        curves_prior = {m: [] for m in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]}
+
+        for e in prior_errors:
+            theta_prior = round(true_target_angle) + e
+            r = estimate_rmse_all_methods(
+                true_target_angle, clutter_angle, N_ant, N_RF,
+                snr_db, theta_prior, 5.0, array_type,
+                mc=Monte_Carlo_test, scan_step=SCAN_STEP
+            )
+            for m, v in zip(["Analog", "Digital", "DFT", "OMP", "TSC-OMP"], r):
+                curves_prior[m].append(v)
+
+        limitation_results[array_type]["prior_errors"] = prior_errors
+        limitation_results[array_type]["prior_curves"] = curves_prior
+
+        plt.figure(figsize=(10, 6))
+        for method in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]:
+            plt.semilogy(prior_errors, curves_prior[method], linewidth=2, label=method, color=method_colors[method])
+        plt.grid(True, which='both', ls='--', alpha=0.6)
+        plt.xlabel('先验角误差 (°)')
+        plt.ylabel('RMSE (°)')
+        plt.title(f'局限性分析：先验误差敏感性 ({array_type}, SNR={snr_db}dB)')
+        plt.legend()
+        plt.tight_layout()
+        plt.savefig(os.path.join(SAVE_DIR, f'limitation_prior_error_{array_type}.png'), dpi=300)
+        plt.show()
+
+        # 6.2 波门宽度敏感性
+        widths = [2, 3, 4, 5, 6, 8, 10]
+        curves_width = {m: [] for m in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]}
+
+        for w in widths:
+            theta_prior = round(true_target_angle)
+            r = estimate_rmse_all_methods(
+                true_target_angle, clutter_angle, N_ant, N_RF,
+                snr_db, theta_prior, float(w), array_type,
+                mc=Monte_Carlo_test, scan_step=SCAN_STEP
+            )
+            for m, v in zip(["Analog", "Digital", "DFT", "OMP", "TSC-OMP"], r):
+                curves_width[m].append(v)
+
+        limitation_results[array_type]["widths"] = widths
+        limitation_results[array_type]["width_curves"] = curves_width
+
+        plt.figure(figsize=(10, 6))
+        for method in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]:
+            plt.semilogy(widths, curves_width[method], linewidth=2, label=method, color=method_colors[method])
+        plt.grid(True, which='both', ls='--', alpha=0.6)
+        plt.xlabel('波门半宽 (°)')
+        plt.ylabel('RMSE (°)')
+        plt.title(f'局限性分析：波门宽度敏感性 ({array_type}, SNR={snr_db}dB)')
+        plt.legend()
+        plt.tight_layout()
+        plt.savefig(os.path.join(SAVE_DIR, f'limitation_window_width_{array_type}.png'), dpi=300)
+        plt.show()
+
+        # 6.3 杂波逼近目标时的性能退化
+        clutter_list = np.array([-25, -20, -15, -10, -8, -6, -4, -2])
+        sep_deg = np.abs(clutter_list - true_target_angle)
+        curves_sep = {m: [] for m in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]}
+
+        for cang in clutter_list:
+            theta_prior = round(true_target_angle)
+            r = estimate_rmse_all_methods(
+                true_target_angle, float(cang), N_ant, N_RF,
+                snr_db, theta_prior, 5.0, array_type,
+                mc=Monte_Carlo_test, scan_step=SCAN_STEP
+            )
+            for m, v in zip(["Analog", "Digital", "DFT", "OMP", "TSC-OMP"], r):
+                curves_sep[m].append(v)
+
+        limitation_results[array_type]["sep_deg"] = sep_deg
+        limitation_results[array_type]["sep_curves"] = curves_sep
+
+        plt.figure(figsize=(10, 6))
+        for method in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]:
+            plt.semilogy(sep_deg, curves_sep[method], linewidth=2, label=method, color=method_colors[method])
+        plt.grid(True, which='both', ls='--', alpha=0.6)
+        plt.xlabel('目标-杂波角距 |θ_t-θ_c| (°)')
+        plt.ylabel('RMSE (°)')
+        plt.title(f'局限性分析：杂波逼近目标时的性能退化 ({array_type})')
+        plt.legend()
+        plt.tight_layout()
+        plt.savefig(os.path.join(SAVE_DIR, f'limitation_clutter_separation_{array_type}.png'), dpi=300)
+        plt.show()
+
+    # ---------------- ULA vs NULA 对照图（重点方法可读性更高） ----------------
+    # 先验误差：比较 TSC-OMP
+    plt.figure(figsize=(10, 6))
+    for array_type in ARRAY_TYPES:
+        x = limitation_results[array_type]["prior_errors"]
+        y = limitation_results[array_type]["prior_curves"]["TSC-OMP"]
+        plt.semilogy(x, y, linewidth=2.5, label=f'TSC-OMP ({array_type})', color=array_colors[array_type])
+    plt.grid(True, which='both', ls='--', alpha=0.6)
+    plt.xlabel('先验角误差 (°)')
+    plt.ylabel('RMSE (°)')
+    plt.title('ULA 与 NULA 对照：TSC-OMP 先验误差敏感性')
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(os.path.join(SAVE_DIR, 'compare_prior_error_TSC_ULA_NULA.png'), dpi=300)
+    plt.show()
+
+    # 波门宽度：比较 TSC-OMP
+    plt.figure(figsize=(10, 6))
+    for array_type in ARRAY_TYPES:
+        x = limitation_results[array_type]["widths"]
+        y = limitation_results[array_type]["width_curves"]["TSC-OMP"]
+        plt.semilogy(x, y, linewidth=2.5, label=f'TSC-OMP ({array_type})', color=array_colors[array_type])
+    plt.grid(True, which='both', ls='--', alpha=0.6)
+    plt.xlabel('波门半宽 (°)')
+    plt.ylabel('RMSE (°)')
+    plt.title('ULA 与 NULA 对照：TSC-OMP 波门宽度敏感性')
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(os.path.join(SAVE_DIR, 'compare_window_width_TSC_ULA_NULA.png'), dpi=300)
+    plt.show()
+
+    # 杂波逼近：比较 TSC-OMP
+    plt.figure(figsize=(10, 6))
+    for array_type in ARRAY_TYPES:
+        x = limitation_results[array_type]["sep_deg"]
+        y = limitation_results[array_type]["sep_curves"]["TSC-OMP"]
+        plt.semilogy(x, y, linewidth=2.5, label=f'TSC-OMP ({array_type})', color=array_colors[array_type])
+    plt.grid(True, which='both', ls='--', alpha=0.6)
+    plt.xlabel('目标-杂波角距 |θ_t-θ_c| (°)')
+    plt.ylabel('RMSE (°)')
+    plt.title('ULA 与 NULA 对照：TSC-OMP 杂波邻近敏感性')
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(os.path.join(SAVE_DIR, 'compare_clutter_sep_TSC_ULA_NULA.png'), dpi=300)
+    plt.show()
+
+    print("局限性分析完成。")
+    return limitation_results
+
+
+# =========================================================
+# 7. 主程序
+# =========================================================
+if __name__ == "__main__":
+    runtime_results, proxy_results = run_complexity_analysis()
+    limitation_results = run_limitation_analysis()
+    print("\n全部分析完成，结果保存在:", SAVE_DIR)
\ No newline at end of file
diff --git a/Graduation Design/RMSE_Test_ula.py b/Graduation Design/RMSE_Test_ula.py
new file mode 100644
index 0000000..9cb50ac
--- /dev/null
+++ b/Graduation Design/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/Graduation Design/main_ula.py b/Graduation Design/main_ula.py
new file mode 100644
index 0000000..e358ed2
--- /dev/null
+++ b/Graduation Design/main_ula.py	
@@ -0,0 +1,280 @@
+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_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_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(所提)')
+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 (所提)')
+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。')
\ No newline at end of file