上传文件至「Code/Python/3.Robust and complexity test」
This commit is contained in:
david1465833828
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# -*- coding: utf-8 -*-
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"""
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TSC-OMP 局限性演示 GIF 生成脚本（顺滑增强版）
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------------------------------------------------
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生成 3 个 GIF：
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1) gif_prior_error_smooth.gif 先验误差敏感性
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2) gif_window_width_smooth.gif 波门宽度敏感性
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3) gif_clutter_approach_smooth.gif 强干扰逼近导致性能退化
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特点：
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- 帧率更高（默认 8 fps）
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- 参数扫描更密，动画更顺滑
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- 独立可运行，不依赖其他脚本
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"""
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.animation import FuncAnimation, PillowWriter
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# =========================================================
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# 0. 全局绘图设置
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# =========================================================
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plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial Unicode MS']
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plt.rcParams['axes.unicode_minus'] = False
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# =========================================================
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# 1. 系统参数
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# =========================================================
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N_ant = 24
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N_RF = 4
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d_lambda = 0.5
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target_angle = 5.04
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clutter_angle_default = -15.0
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noise_power = 1.0
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INR_dB = 30.0
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INR_linear = 10 ** (INR_dB / 10)
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L_snapshots = 100
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theta_scan = np.arange(-90, 90.01, 0.1)
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# Monte Carlo 参数（如果电脑慢，可以把 30 改成 20）
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Monte_Carlo = 30
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SNR_dB = 0.0 # GIF 中用于 RMSE 演示的默认 SNR
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# =========================================================
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# 2. GIF 平滑参数（核心改进）
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# =========================================================
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GIF_FPS = 8 # 帧率：越大越顺滑，但文件也更大
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GIF_INTERVAL = 80 # 播放间隔（毫秒）
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FIG_DPI = 110
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# 线条样式
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LINE_WIDTH = 2.6
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MARKER_SIZE = 7
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# 输出目录
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SAVE_DIR = "gif_outputs"
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os.makedirs(SAVE_DIR, exist_ok=True)
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# 随机种子
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GLOBAL_SEED = 2025
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# =========================================================
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# 3. 阵列导向矢量（ULA）
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# =========================================================
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def gen_a(theta_deg, N=N_ant, d=d_lambda):
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"""
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ULA 导向矢量
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"""
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theta_rad = np.deg2rad(theta_deg)
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n_idx = np.arange(-(N - 1) / 2, (N - 1) / 2 + 1)
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return np.exp(1j * 2 * np.pi * d * n_idx * np.sin(theta_rad)).reshape(-1, 1)
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# =========================================================
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# 4. 字典构造
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# =========================================================
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def build_dictionary(angle_grid):
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A = np.zeros((N_ant, len(angle_grid)), dtype=complex)
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for i, th in enumerate(angle_grid):
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A[:, i:i+1] = gen_a(th) / np.sqrt(N_ant)
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return A
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# =========================================================
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# 5. OMP 选择射频矩阵
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# =========================================================
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def omp_select(w_target, A_dic, Nrf):
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"""
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基础 OMP：根据目标权向量 w_target 在字典 A_dic 中贪婪选择 Nrf 个原子
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"""
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residual = w_target.copy()
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selected = []
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F_RF = np.zeros((A_dic.shape[0], 0), dtype=complex)
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for _ in range(Nrf):
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proj = A_dic.conj().T @ residual
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idx = int(np.argmax(np.abs(proj)))
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selected.append(idx)
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F_RF = A_dic[:, selected]
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residual = w_target - F_RF @ (np.linalg.pinv(F_RF) @ w_target)
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return F_RF
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# =========================================================
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# 6. 计算静态先验数字权值（MVDR）
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# =========================================================
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def calc_prior_weight(a_target, a_clutter, sig_power_static=1.0):
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"""
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用静态协方差设计数字先验权值
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"""
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R_static = (noise_power * np.eye(N_ant) +
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sig_power_static * (a_target @ a_target.conj().T) +
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INR_linear * (a_clutter @ a_clutter.conj().T))
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dl = 0.01 * np.trace(R_static) / N_ant
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R_static_dl = R_static + dl * np.eye(N_ant)
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inv_R = np.linalg.inv(R_static_dl)
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w_prior = inv_R @ a_target / (a_target.conj().T @ inv_R @ a_target)
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w_prior = w_prior / np.linalg.norm(w_prior)
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return w_prior, R_static
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# =========================================================
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# 7. 构造 TSC-OMP 混合波束
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# =========================================================
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def build_tsc_structure(target_ang, clutter_ang, theta_prior, window_half_width):
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"""
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根据目标角、干扰角、先验中心、波门半宽，构造 TSC-OMP 混合波束
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"""
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a_target = gen_a(target_ang)
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a_clutter = gen_a(clutter_ang)
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# 局部约束字典（TSC）
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dic_angles = np.arange(theta_prior - window_half_width,
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theta_prior + window_half_width + 1e-9,
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0.1)
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A_dic = build_dictionary(dic_angles)
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# 静态先验权值
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w_prior, R_static = calc_prior_weight(a_target, a_clutter, sig_power_static=1.0)
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# OMP 选模拟 RF
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F_RF = omp_select(w_prior, A_dic, N_RF)
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# 基带 MVDR
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a_eff = F_RF.conj().T @ a_target
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R_eff = F_RF.conj().T @ R_static @ F_RF
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dl = 0.01 * np.trace(R_eff) / N_RF
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R_eff_dl = R_eff + dl * np.eye(N_RF)
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inv_R_eff = np.linalg.inv(R_eff_dl)
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F_BB = inv_R_eff @ a_eff / (a_eff.conj().T @ inv_R_eff @ a_eff)
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w_hybrid = F_RF @ F_BB
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w_hybrid = w_hybrid / np.linalg.norm(w_hybrid)
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return {
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"a_target": a_target,
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"a_clutter": a_clutter,
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"F_RF": F_RF,
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"w_tsc": w_hybrid
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}
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# =========================================================
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# 8. 方向图
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# =========================================================
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def calc_beam_pattern(w, scan_grid):
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bp = np.zeros(len(scan_grid))
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for i, th in enumerate(scan_grid):
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a = gen_a(th)
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bp[i] = np.abs(a.conj().T @ w).item() ** 2
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bp_db = 10 * np.log10(bp / np.max(bp) + 1e-12)
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return bp_db
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# =========================================================
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# 9. 输出 SINR
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# =========================================================
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def calc_output_sinr(w, a_target, a_clutter, sig_power):
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R_i_n = noise_power * np.eye(N_ant) + INR_linear * (a_clutter @ a_clutter.conj().T)
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numerator = sig_power * np.abs(w.conj().T @ a_target).item() ** 2
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denominator = np.real((w.conj().T @ R_i_n @ w).item())
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return 10 * np.log10(numerator / (denominator + 1e-12) + 1e-12)
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# =========================================================
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# 10. 单次 Monte Carlo 下的 TSC-OMP 测角 RMSE
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# =========================================================
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def estimate_rmse_tsc(target_ang, clutter_ang, theta_prior, window_half_width,
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snr_db, monte_carlo=Monte_Carlo, seed_offset=0):
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"""
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用低维 Capon 光谱做测角，返回 RMSE
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为了兼顾速度和稳定性，这里做了适度的快速化处理
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"""
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rng = np.random.default_rng(GLOBAL_SEED + seed_offset)
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structure = build_tsc_structure(target_ang, clutter_ang, theta_prior, window_half_width)
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F_RF = structure["F_RF"]
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a_target = structure["a_target"]
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a_clutter = structure["a_clutter"]
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sig_power = 10 ** (snr_db / 10)
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# 搜索网格只在波门内搜索
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scan_grid = np.arange(theta_prior - window_half_width,
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theta_prior + window_half_width + 1e-9,
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0.05)
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A_scan = np.hstack([gen_a(th) for th in scan_grid])
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A_eff = F_RF.conj().T @ A_scan
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err_sq = []
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for _ in range(monte_carlo):
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s_t = np.sqrt(sig_power / 2) * (rng.standard_normal((1, L_snapshots)) +
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1j * rng.standard_normal((1, L_snapshots)))
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s_c = np.sqrt(INR_linear / 2) * (rng.standard_normal((1, L_snapshots)) +
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1j * rng.standard_normal((1, L_snapshots)))
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n_t = np.sqrt(noise_power / 2) * (rng.standard_normal((N_ant, L_snapshots)) +
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1j * rng.standard_normal((N_ant, L_snapshots)))
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X = a_target @ s_t + a_clutter @ s_c + n_t
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Y = F_RF.conj().T @ X
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R_bb = (Y @ Y.conj().T) / L_snapshots
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# 对角加载（提升稳定性）
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gamma = 0.05 * np.trace(R_bb) / N_RF
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R_bb_dl = R_bb + gamma * np.eye(N_RF)
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inv_R = np.linalg.inv(R_bb_dl)
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# Capon 谱：P(theta)=1/(a^H inv(R) a)
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temp = inv_R @ A_eff
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denom = np.sum(np.conj(A_eff) * temp, axis=0)
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spectrum = 1.0 / (np.abs(denom) + 1e-12)
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est_ang = scan_grid[np.argmax(spectrum)]
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err_sq.append((est_ang - target_ang) ** 2)
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return float(np.sqrt(np.mean(err_sq)))
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# =========================================================
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# 11. 一些辅助绘图函数
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# =========================================================
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def beautify_axes(ax):
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ax.grid(True, linestyle='--', alpha=0.5)
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ax.tick_params(labelsize=11)
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# =========================================================
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# 12. GIF 1：先验误差敏感性
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# =========================================================
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def make_prior_error_gif():
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print("正在生成 GIF 1：先验误差敏感性 ...")
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# 帧更密，动画更顺滑
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prior_errors = np.arange(-8.0, 8.01, 0.5)
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patterns = []
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sinrs = []
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rmses = []
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for i, e in enumerate(prior_errors):
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theta_prior = round(target_angle) + e
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structure = build_tsc_structure(target_angle, clutter_angle_default,
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theta_prior, 5.0)
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bp = calc_beam_pattern(structure["w_tsc"], theta_scan)
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sinr = calc_output_sinr(structure["w_tsc"],
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structure["a_target"],
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structure["a_clutter"],
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10 ** (SNR_dB / 10))
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rmse = estimate_rmse_tsc(target_angle, clutter_angle_default,
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theta_prior, 5.0, SNR_dB,
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monte_carlo=Monte_Carlo,
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seed_offset=1000 + i)
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patterns.append(bp)
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sinrs.append(sinr)
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rmses.append(rmse)
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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ax1, ax2 = axes
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line_bp, = ax1.plot([], [], lw=LINE_WIDTH, color="#0072BD", label="TSC-OMP方向图")
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ax1.axvline(target_angle, color='g', linestyle='--', lw=2, label="真实目标")
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prior_line = ax1.axvline(target_angle, color='m', linestyle='--', lw=2, label="先验中心")
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ax1.set_xlim([-20, 20])
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ax1.set_ylim([-70, 2])
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ax1.set_xlabel("角度 θ (°)", fontsize=13)
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ax1.set_ylabel("归一化增益 (dB)", fontsize=13)
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ax1.set_title("先验误差增大时的性能退化", fontsize=15, fontweight='bold')
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beautify_axes(ax1)
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ax1.legend(fontsize=11)
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line_rmse, = ax2.plot([], [], color="#D95319", lw=LINE_WIDTH,
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marker='o', markersize=MARKER_SIZE, label="RMSE")
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current_pt, = ax2.plot([], [], 'ro', markersize=9)
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ax2.set_xlim(prior_errors[0], prior_errors[-1])
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ax2.set_ylim(0, max(rmses) * 1.25 + 0.1)
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ax2.set_xlabel("先验角误差 (°)", fontsize=13)
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ax2.set_ylabel("测角 RMSE (°)", fontsize=13)
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ax2.set_title("先验误差越大，RMSE 越容易升高", fontsize=15, fontweight='bold')
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beautify_axes(ax2)
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ax2.legend(fontsize=11)
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info = ax1.text(0.02, 0.96, '', transform=ax1.transAxes, va='top',
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bbox=dict(boxstyle='round', facecolor='white', alpha=0.88), fontsize=11)
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def update(frame):
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e = prior_errors[frame]
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theta_prior = round(target_angle) + e
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line_bp.set_data(theta_scan, patterns[frame])
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prior_line.set_xdata([theta_prior, theta_prior])
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line_rmse.set_data(prior_errors[:frame+1], rmses[:frame+1])
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current_pt.set_data([e], [rmses[frame]])
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info.set_text(
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f"先验角误差 = {e:+.2f}°\n"
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f"先验中心 = {theta_prior:.2f}°\n"
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f"输出 SINR = {sinrs[frame]:.2f} dB\n"
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f"RMSE = {rmses[frame]:.3f}°"
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)
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return line_bp, prior_line, line_rmse, current_pt, info
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ani = FuncAnimation(fig, update, frames=len(prior_errors),
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interval=GIF_INTERVAL, blit=False, repeat=True)
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save_path = os.path.join(SAVE_DIR, "gif_prior_error_smooth.gif")
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ani.save(save_path, writer=PillowWriter(fps=GIF_FPS), dpi=FIG_DPI)
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plt.close(fig)
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print(f"GIF 1 保存完成：{save_path}")
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# =========================================================
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# 13. GIF 2：波门宽度敏感性
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# =========================================================
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def make_window_width_gif():
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print("正在生成 GIF 2：波门宽度敏感性 ...")
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widths = np.arange(2.0, 10.01, 0.5)
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patterns = []
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sinrs = []
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rmses = []
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for i, w in enumerate(widths):
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theta_prior = round(target_angle)
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structure = build_tsc_structure(target_angle, clutter_angle_default,
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theta_prior, w)
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bp = calc_beam_pattern(structure["w_tsc"], theta_scan)
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sinr = calc_output_sinr(structure["w_tsc"],
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structure["a_target"],
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structure["a_clutter"],
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10 ** (SNR_dB / 10))
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rmse = estimate_rmse_tsc(target_angle, clutter_angle_default,
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theta_prior, w, SNR_dB,
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monte_carlo=Monte_Carlo,
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seed_offset=2000 + i)
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patterns.append(bp)
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sinrs.append(sinr)
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rmses.append(rmse)
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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ax1, ax2 = axes
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line_bp, = ax1.plot([], [], lw=LINE_WIDTH, color="#0072BD", label="TSC-OMP方向图")
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ax1.axvline(target_angle, color='g', linestyle='--', lw=2, label="真实目标")
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gate_left = ax1.axvline(target_angle - widths[0], color='m', linestyle=':', lw=2)
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gate_right = ax1.axvline(target_angle + widths[0], color='m', linestyle=':', lw=2, label="波门边界")
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ax1.set_xlim([-20, 20])
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ax1.set_ylim([-70, 2])
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ax1.set_xlabel("角度 θ (°)", fontsize=13)
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ax1.set_ylabel("归一化增益 (dB)", fontsize=13)
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ax1.set_title("波门宽度变化对方向图的影响", fontsize=15, fontweight='bold')
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beautify_axes(ax1)
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ax1.legend(fontsize=11)
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line_rmse, = ax2.plot([], [], color="#D95319", lw=LINE_WIDTH,
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marker='o', markersize=MARKER_SIZE, label="RMSE")
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current_pt, = ax2.plot([], [], 'ro', markersize=9)
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ax2.set_xlim(widths[0], widths[-1])
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ax2.set_ylim(0, max(rmses) * 1.25 + 0.1)
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ax2.set_xlabel("波门半宽 (°)", fontsize=13)
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ax2.set_ylabel("测角 RMSE (°)", fontsize=13)
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ax2.set_title("波门过窄或过宽都会带来性能损失", fontsize=15, fontweight='bold')
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beautify_axes(ax2)
|
||||
ax2.legend(fontsize=11)
|
||||
|
||||
info = ax1.text(0.02, 0.96, '', transform=ax1.transAxes, va='top',
|
||||
bbox=dict(boxstyle='round', facecolor='white', alpha=0.88), fontsize=11)
|
||||
|
||||
def update(frame):
|
||||
w = widths[frame]
|
||||
|
||||
line_bp.set_data(theta_scan, patterns[frame])
|
||||
gate_left.set_xdata([target_angle - w, target_angle - w])
|
||||
gate_right.set_xdata([target_angle + w, target_angle + w])
|
||||
|
||||
line_rmse.set_data(widths[:frame+1], rmses[:frame+1])
|
||||
current_pt.set_data([w], [rmses[frame]])
|
||||
|
||||
info.set_text(
|
||||
f"波门半宽 = ±{w:.2f}°\n"
|
||||
f"输出 SINR = {sinrs[frame]:.2f} dB\n"
|
||||
f"RMSE = {rmses[frame]:.3f}°"
|
||||
)
|
||||
|
||||
return line_bp, gate_left, gate_right, line_rmse, current_pt, info
|
||||
|
||||
ani = FuncAnimation(fig, update, frames=len(widths),
|
||||
interval=GIF_INTERVAL, blit=False, repeat=True)
|
||||
|
||||
save_path = os.path.join(SAVE_DIR, "gif_window_width_smooth.gif")
|
||||
ani.save(save_path, writer=PillowWriter(fps=GIF_FPS), dpi=FIG_DPI)
|
||||
plt.close(fig)
|
||||
|
||||
print(f"GIF 2 保存完成：{save_path}")
|
||||
|
||||
# =========================================================
|
||||
# 14. GIF 3：强干扰逼近目标
|
||||
# =========================================================
|
||||
def make_clutter_approach_gif():
|
||||
print("正在生成 GIF 3：强干扰逼近目标时的性能退化 ...")
|
||||
|
||||
# 更密的干扰角扫描，让动画更顺滑
|
||||
clutter_list = np.arange(-25.0, -1.99, 0.5)
|
||||
|
||||
patterns = []
|
||||
sinrs = []
|
||||
rmses = []
|
||||
separations = []
|
||||
|
||||
for i, cang in enumerate(clutter_list):
|
||||
theta_prior = round(target_angle)
|
||||
structure = build_tsc_structure(target_angle, cang,
|
||||
theta_prior, 5.0)
|
||||
bp = calc_beam_pattern(structure["w_tsc"], theta_scan)
|
||||
sinr = calc_output_sinr(structure["w_tsc"],
|
||||
structure["a_target"],
|
||||
structure["a_clutter"],
|
||||
10 ** (SNR_dB / 10))
|
||||
rmse = estimate_rmse_tsc(target_angle, cang,
|
||||
theta_prior, 5.0, SNR_dB,
|
||||
monte_carlo=Monte_Carlo,
|
||||
seed_offset=3000 + i)
|
||||
|
||||
patterns.append(bp)
|
||||
sinrs.append(sinr)
|
||||
rmses.append(rmse)
|
||||
separations.append(abs(target_angle - cang))
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
||||
ax1, ax2 = axes
|
||||
|
||||
line_bp, = ax1.plot([], [], lw=LINE_WIDTH, color="#0072BD", label="TSC-OMP方向图")
|
||||
ax1.axvline(target_angle, color='g', linestyle='--', lw=2, label="目标")
|
||||
clutter_line = ax1.axvline(clutter_list[0], color='r', linestyle='--', lw=2, label="强干扰")
|
||||
ax1.set_xlim([-40, 20])
|
||||
ax1.set_ylim([-70, 2])
|
||||
ax1.set_xlabel("角度 θ (°)", fontsize=13)
|
||||
ax1.set_ylabel("归一化增益 (dB)", fontsize=13)
|
||||
ax1.set_title("强干扰逐步逼近目标时的方向图退化", fontsize=15, fontweight='bold')
|
||||
beautify_axes(ax1)
|
||||
ax1.legend(fontsize=11)
|
||||
|
||||
line_rmse, = ax2.plot([], [], color="#D95319", lw=LINE_WIDTH,
|
||||
marker='o', markersize=MARKER_SIZE, label="RMSE")
|
||||
current_pt, = ax2.plot([], [], 'ro', markersize=9)
|
||||
ax2.set_xlim(max(separations), min(separations))
|
||||
ax2.invert_xaxis()
|
||||
ax2.set_ylim(0, max(rmses) * 1.25 + 0.1)
|
||||
ax2.set_xlabel("目标-干扰角距 |θt-θc| (°)", fontsize=13)
|
||||
ax2.set_ylabel("测角 RMSE (°)", fontsize=13)
|
||||
ax2.set_title("角距减小后，可分性下降，RMSE 增大", fontsize=15, fontweight='bold')
|
||||
beautify_axes(ax2)
|
||||
ax2.legend(fontsize=11)
|
||||
|
||||
info = ax1.text(0.02, 0.96, '', transform=ax1.transAxes, va='top',
|
||||
bbox=dict(boxstyle='round', facecolor='white', alpha=0.88), fontsize=11)
|
||||
|
||||
def update(frame):
|
||||
cang = clutter_list[frame]
|
||||
sep = separations[frame]
|
||||
|
||||
line_bp.set_data(theta_scan, patterns[frame])
|
||||
clutter_line.set_xdata([cang, cang])
|
||||
|
||||
line_rmse.set_data(separations[:frame+1], rmses[:frame+1])
|
||||
current_pt.set_data([sep], [rmses[frame]])
|
||||
|
||||
info.set_text(
|
||||
f"干扰角度 = {cang:.2f}°\n"
|
||||
f"目标-干扰角距 = {sep:.2f}°\n"
|
||||
f"输出 SINR = {sinrs[frame]:.2f} dB\n"
|
||||
f"RMSE = {rmses[frame]:.3f}°"
|
||||
)
|
||||
|
||||
return line_bp, clutter_line, line_rmse, current_pt, info
|
||||
|
||||
ani = FuncAnimation(fig, update, frames=len(clutter_list),
|
||||
interval=GIF_INTERVAL, blit=False, repeat=True)
|
||||
|
||||
save_path = os.path.join(SAVE_DIR, "gif_clutter_approach_smooth.gif")
|
||||
ani.save(save_path, writer=PillowWriter(fps=GIF_FPS), dpi=FIG_DPI)
|
||||
plt.close(fig)
|
||||
|
||||
print(f"GIF 3 保存完成：{save_path}")
|
||||
|
||||
# =========================================================
|
||||
# 15. 主函数
|
||||
# =========================================================
|
||||
def main():
|
||||
print("====================================================")
|
||||
print("开始生成 TSC-OMP 局限性分析 GIF（顺滑增强版）")
|
||||
print("输出目录：", os.path.abspath(SAVE_DIR))
|
||||
print(f"GIF 帧率：{GIF_FPS} fps")
|
||||
print(f"动画间隔：{GIF_INTERVAL} ms")
|
||||
print("====================================================")
|
||||
|
||||
make_prior_error_gif()
|
||||
make_window_width_gif()
|
||||
make_clutter_approach_gif()
|
||||
|
||||
print("====================================================")
|
||||
print("全部 GIF 生成完毕！")
|
||||
print("生成文件：")
|
||||
print("1) gif_prior_error_smooth.gif")
|
||||
print("2) gif_window_width_smooth.gif")
|
||||
print("3) gif_clutter_approach_smooth.gif")
|
||||
print("====================================================")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user