上传文件至「Code/Python/1.ULA」
This commit is contained in:
david1465833828
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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# ==========================================
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# 0. 全局绘图参数与 CUDA 引擎初始化
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# ==========================================
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plt.rcParams['axes.unicode_minus'] = False
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
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plt.rcParams['mathtext.fontset'] = 'stix'
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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torch.manual_seed(2024)
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N_ant, N_RF, d_lambda = 24, 4, 0.5
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clutter_angle = -15.0
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noise_power = 1.0
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INR_linear = 10**(30/10)
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def gen_a(theta_deg):
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theta_rad = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device))
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n_idx = torch.arange(-(N_ant-1)/2, (N_ant-1)/2 + 0.1, device=device)
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return torch.exp(1j * 2 * torch.pi * d_lambda * n_idx * torch.sin(theta_rad)).unsqueeze(1)
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a_clutter = gen_a(clutter_angle)
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R_interference = noise_power * torch.eye(N_ant, device=device) + INR_linear * (a_clutter @ a_clutter.mH)
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dic_angles_full = torch.arange(-90, 90.5, 0.5, device=device)
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A_dic_full = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_angles_full], dim=1)
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angle_ranges = [np.arange(0, 11), np.arange(11, 21), np.arange(21, 31), np.arange(31, 41)]
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snr_test_list = [0, 5, 10, 15, 20]
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Monte_Carlo = 100
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L_snapshots = 100
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colors = ['#0072BD', '#D95319', '#EDB120', '#7E2F8E', '#77AC30']
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def run_omp(w_opt, A_dic):
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res = w_opt.clone()
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F_RF_list = []
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for k in range(N_RF):
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idx = torch.argmax(torch.abs(A_dic.mH @ res))
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F_RF_list.append(A_dic[:, idx:idx+1])
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F_RF_mat = torch.cat(F_RF_list, dim=1)
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res = w_opt - F_RF_mat @ (torch.linalg.pinv(F_RF_mat) @ w_opt)
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return F_RF_mat
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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})...")
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start_time = time.time()
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# 取消了这里原本的 fig, axs = plt.subplots(2, 2)
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# 改为在循环内部动态创建单图
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for fig_idx, test_angles in enumerate(angle_ranges):
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print(f"正在计算扇区 {test_angles[0]}° ~ {test_angles[-1]}° ...")
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# 【修改点 1】：为每一个扇区单独生成一张独立画布
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fig, ax = plt.subplots(figsize=(9, 7))
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RMSE_DFT_All = np.zeros((len(snr_test_list), len(test_angles)))
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RMSE_FULL_All = np.zeros((len(snr_test_list), len(test_angles)))
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RMSE_TSC_All = np.zeros((len(snr_test_list), len(test_angles)))
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for snr_idx, snr_dB in enumerate(snr_test_list):
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sig_p = 10**(snr_dB/10)
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for a_idx, t_angle in enumerate(test_angles):
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true_target_angle = t_angle + 0.04
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theta_prior = round(true_target_angle)
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window = 5
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dic_tsc = torch.arange(theta_prior - window, theta_prior + window + 0.1, 0.1, device=device)
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A_dic_tsc = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_tsc], dim=1)
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angles_dft = torch.linspace(theta_prior - window, theta_prior + window, N_RF, device=device)
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F_RF_dft = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in angles_dft], dim=1)
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a_prior_broad = torch.zeros((N_ant, 1), dtype=torch.complex64, device=device)
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for tb in torch.arange(theta_prior - window, theta_prior + window + 0.1, 0.5, device=device):
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a_prior_broad += gen_a(tb)
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a_prior_broad /= torch.norm(a_prior_broad)
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w_dig_prior = torch.linalg.inv(R_interference) @ a_prior_broad
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w_dig_prior /= torch.norm(w_dig_prior)
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F_RF_full = run_omp(w_dig_prior, A_dic_full)
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F_RF_tsc = run_omp(w_dig_prior, A_dic_tsc)
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scan_grid = torch.arange(theta_prior - window, theta_prior + window + 0.01, 0.01, device=device)
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A_scan = torch.cat([gen_a(th) for th in scan_grid], dim=1)
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A_eff_dft = F_RF_dft.mH @ A_scan
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A_eff_full = F_RF_full.mH @ A_scan
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A_eff_tsc = F_RF_tsc.mH @ A_scan
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a_target = gen_a(true_target_angle)
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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))
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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))
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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))
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X = a_target.unsqueeze(0) * s_t + a_clutter.unsqueeze(0) * s_c + n_t
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def process_hybrid(F_RF, A_eff):
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Y = F_RF.mH.unsqueeze(0) @ X
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R = (Y @ Y.mH) / L_snapshots
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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)
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inv_R = torch.linalg.inv(R)
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P = 1.0 / torch.abs(torch.einsum('si, bij, js -> bs', A_eff.conj().T, inv_R, A_eff))
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return np.sqrt(np.mean(((scan_grid[torch.argmax(P, dim=1)] - true_target_angle)**2).cpu().numpy()))
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RMSE_DFT_All[snr_idx, a_idx] = process_hybrid(F_RF_dft, A_eff_dft)
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RMSE_FULL_All[snr_idx, a_idx] = process_hybrid(F_RF_full, A_eff_full)
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RMSE_TSC_All[snr_idx, a_idx] = process_hybrid(F_RF_tsc, A_eff_tsc)
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c_str = colors[snr_idx]
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lbl_suffix = f" (SNR={snr_dB}dB)"
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ax.semilogy(test_angles, RMSE_DFT_All[snr_idx, :], '--^', color=c_str, linewidth=1.5, label='DFT'+lbl_suffix)
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ax.semilogy(test_angles, RMSE_FULL_All[snr_idx, :], ':x', color=c_str, linewidth=2, label='传统OMP'+lbl_suffix)
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ax.semilogy(test_angles, RMSE_TSC_All[snr_idx, :], '-o', color=c_str, linewidth=2.5, label='TSC-OMP'+lbl_suffix)
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ax.set_xlim([test_angles[0], test_angles[-1]])
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ax.set_xticks(test_angles)
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ax.grid(True, which="both", ls="--", alpha=0.5)
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ax.set_ylim([1e-3, 20])
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ax.set_xlabel('目标真实出现角度 $\\theta (\\circ)$', fontsize=12, fontweight='bold')
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ax.set_ylabel(r'测角 RMSE $(^\circ)$', fontsize=12, fontweight='bold')
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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')
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# 【修改点 2】：独立设置每张图的图例。
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plt.tight_layout()
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fig.subplots_adjust(bottom=0.25)
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# 【关键调整】：将 bbox_to_anchor 的 Y 坐标从 -0.15 提上来，改为 -0.08
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# 这样图例就会离 X 轴更近，避开底部操作条
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ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.10), ncol=3, fontsize=10)
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# 【修改点 3】：自动保存
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file_name = f'RMSE_Sector_{test_angles[0]}_{test_angles[-1]}.png'
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plt.savefig(file_name, dpi=300, bbox_inches='tight')
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print(f" 已保存独立图表: {file_name}")
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print(f"全扇区计算完毕！总耗时: {time.time() - start_time:.4f} 秒。")
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# 一次性在屏幕上弹出生成的4张独立图表
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plt.show()
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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# ==========================================
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# 0. 全局设置与 CUDA 引擎
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# ==========================================
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plt.rcParams['axes.unicode_minus'] = False
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# 锁定随机数种子
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torch.manual_seed(2024)
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np.random.seed(2024)
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# 颜色设置
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c_ana = '#77AC30'
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c_dig = '#000000'
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c_dft = '#4DBEEE'
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c_omp = '#D95319'
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c_tsc = '#0072BD'
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# ==========================================
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# 1. 物理层参数初始化 (标准 ULA)
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# ==========================================
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N_ant, N_RF = 24, 4 #24根天线，4个射频链路
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#target_angle = float(input("请输入目标位置角度（°）（范围0-20°）：")) #目标位置
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#clutter_angle = float(input("请输入干扰位置角度（°）（范围-3°~-20°）：")) #干扰位置
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target_angle = 5.0
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clutter_angle = -15.0
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L_snapshots = 100
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noise_power = 1.0
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INR_linear = 10**(30/10)
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d_lambda = 0.5 # 经典的半波长间距
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def gen_a(theta_deg):
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"""标准的均匀线阵 (ULA) 导向矢量"""
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theta_rad = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device))
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# 以阵列中心为参考点
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n_idx = torch.arange(-(N_ant-1)/2, (N_ant-1)/2 + 0.1, device=device)
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return torch.exp(1j * 2 * torch.pi * d_lambda * n_idx * torch.sin(theta_rad)).unsqueeze(1)
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a_target = gen_a(target_angle)
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a_clutter = gen_a(clutter_angle)
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# ==========================================
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# 2. 动态生成目标约束扇区与字典
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# ==========================================
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theta_coarse_prior = round(target_angle)
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window_half_width = 5.0
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dic_angles_constrained = torch.arange(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width + 0.1, 0.1, device=device)
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A_dic_constrained = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_angles_constrained], dim=1)
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dic_angles_full = torch.arange(-90, 90.5, 0.5, device=device)
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A_dic_full = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in dic_angles_full], dim=1)
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angles_dft = torch.linspace(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width, N_RF, device=device)
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F_RF_dft = torch.cat([gen_a(th) / np.sqrt(N_ant) for th in angles_dft], dim=1)
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# ==========================================
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# 3. 提取防发散的慢时间先验射频矩阵 F_RF
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# ==========================================
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print('正在进行慢时间 (Slow-Time) 模拟射频网络硬件配置...')
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a_prior_broad = torch.zeros((N_ant, 1), dtype=torch.complex64, device=device)
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for theta_b in torch.arange(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width + 0.1, 0.5, device=device):
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a_prior_broad += gen_a(theta_b)
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a_prior_broad /= torch.norm(a_prior_broad)
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R_interference_prior = noise_power * torch.eye(N_ant, device=device) + INR_linear * (a_clutter @ a_clutter.mH)
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w_digital_prior = torch.linalg.inv(R_interference_prior) @ a_prior_broad
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w_digital_prior /= torch.norm(w_digital_prior)
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def extract_omp(w_opt, A_dic):
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res = w_opt.clone()
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F_RF_mat = None
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for k in range(N_RF):
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proj = A_dic.mH @ res
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idx = torch.argmax(torch.abs(proj))
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new_atom = A_dic[:, idx:idx+1]
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F_RF_mat = new_atom if F_RF_mat is None else torch.cat((F_RF_mat, new_atom), dim=1)
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res = w_digital_prior - F_RF_mat @ (torch.linalg.pinv(F_RF_mat) @ w_digital_prior)
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return F_RF_mat
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F_RF_omp_full_static = extract_omp(w_digital_prior, A_dic_full)
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F_RF_omp_static = extract_omp(w_digital_prior, A_dic_constrained)
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# ==========================================
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# 4. 计算波束方向图与理论 SINR
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# ==========================================
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print('正在计算静态波束方向图与理论 SINR...')
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w_analog = a_target / np.sqrt(N_ant)
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def calc_hybrid_weight(F_RF, R_in_stat):
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a_eff = F_RF.mH @ a_target
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R_eff = F_RF.mH @ R_in_stat @ F_RF
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R_eff += (0.05 * torch.trace(R_eff).real / N_RF) * torch.eye(N_RF, device=device)
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inv_R_eff = torch.linalg.inv(R_eff)
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F_BB = (inv_R_eff @ a_eff) / (a_eff.mH @ inv_R_eff @ a_eff)
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w_hyb = F_RF @ F_BB
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return w_hyb / torch.norm(w_hyb)
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sig_power_static = 10**(0/10)
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# 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
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R_in_static = noise_power * torch.eye(N_ant, device=device) + \
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sig_power_static * (a_target @ a_target.mH) + \
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INR_linear * (a_clutter @ a_clutter.mH)
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w_hybrid_dft = calc_hybrid_weight(F_RF_dft, R_in_static)
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w_hybrid_omp_full = calc_hybrid_weight(F_RF_omp_full_static, R_in_static)
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w_hybrid_omp = calc_hybrid_weight(F_RF_omp_static, R_in_static)
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R_in_static_dl = R_in_static + (0.01 * torch.trace(R_in_static).real / N_ant) * torch.eye(N_ant, device=device)
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inv_R_stat = torch.linalg.inv(R_in_static_dl)
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w_digital_ideal = (inv_R_stat @ a_target) / (a_target.mH @ inv_R_stat @ a_target)
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w_digital_ideal /= torch.norm(w_digital_ideal)
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theta_scan = torch.arange(-90, 90.1, 0.1, device=device)
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A_scan_full = torch.cat([gen_a(th) for th in theta_scan], dim=1)
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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))
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BP_Ana = get_bp(w_analog).cpu().numpy()
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BP_Dig = get_bp(w_digital_ideal).cpu().numpy()
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BP_DFT = get_bp(w_hybrid_dft).cpu().numpy()
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BP_OMP_FULL = get_bp(w_hybrid_omp_full).cpu().numpy()
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BP_OMP = get_bp(w_hybrid_omp).cpu().numpy()
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theta_scan_np = theta_scan.cpu().numpy()
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SNR_dB_range = np.arange(-10, 22, 2)
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SINR_Ana, SINR_Dig, SINR_DFT, SINR_OMP_FULL, SINR_OMP = [], [], [], [], []
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for snr in SNR_dB_range:
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sig_p = 10**(snr/10)
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def calc_sinr(w): return (sig_p * torch.abs(w.mH @ a_target)**2) / torch.real(w.mH @ R_interference_prior @ w)
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SINR_Ana.append(10 * torch.log10(calc_sinr(w_analog)).item())
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SINR_Dig.append(10 * torch.log10(calc_sinr(w_digital_ideal)).item())
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SINR_DFT.append(10 * torch.log10(calc_sinr(w_hybrid_dft)).item())
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SINR_OMP_FULL.append(10 * torch.log10(calc_sinr(w_hybrid_omp_full)).item())
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SINR_OMP.append(10 * torch.log10(calc_sinr(w_hybrid_omp)).item())
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# ==========================================
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# 5. 基于张量 Batch 操作的极致蒙特卡洛评估
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# ==========================================
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print('正在启动 CUDA 高维张量批量计算 (Batched Evaluation)...')
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Monte_Carlo = 500
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scan_grid = torch.arange(theta_coarse_prior - window_half_width, theta_coarse_prior + window_half_width + 0.01, 0.01, device=device)
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A_scan = torch.cat([gen_a(th) for th in scan_grid], dim=1)
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A_eff_dft = F_RF_dft.mH @ A_scan
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A_eff_omp_full = F_RF_omp_full_static.mH @ A_scan
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A_eff_omp = F_RF_omp_static.mH @ A_scan
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RMSE_Ana, RMSE_Dig, RMSE_DFT, RMSE_OMP_FULL, RMSE_OMP = [], [], [], [], []
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start_time = time.time()
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for snr in SNR_dB_range:
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sig_power = 10**(snr/10)
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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))
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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。')
|
||||
Reference in New Issue
Block a user