add python simulation for mic array localization
This commit is contained in:
353
Simulation/mic_array_sim.py
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353
Simulation/mic_array_sim.py
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.fft import fft, ifft
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from scipy.signal import chirp
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import random
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import warnings
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import time
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# 忽略除零警告，我们在 PHAT 中会处理 epsilon
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warnings.filterwarnings('ignore')
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class MicArraySimulator:
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def __init__(self, fs=16000, mic_dist=0.05, c_sound=343.0):
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"""
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初始化麦克风阵列仿真器
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:param fs: 采样率 (Hz)
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:param mic_dist: 麦克风间距 (m), 等边三角形边长
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:param c_sound: 声速 (m/s)
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"""
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self.fs = fs
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self.c = c_sound
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self.d = mic_dist
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# 1. 定义麦克风坐标 (等边三角形，A 在前，B 左后，C 右后)
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# 原点为阵列中心（重心）
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h = np.sqrt(3) / 2 * self.d # 三角形高
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# A (Front): (0, 2/3 * h)
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# B (Left): (-d/2, -1/3 * h)
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# C (Right): (d/2, -1/3 * h)
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self.mic_pos = np.array([
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[0, 2/3 * h], # Mic A
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[-self.d/2, -1/3 * h], # Mic B
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[self.d/2, -1/3 * h] # Mic C
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])
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# 预计算基线向量 (用于线性方程法)
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# 使用 AB 和 AC 作为独立基
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self.vec_AB = self.mic_pos[1] - self.mic_pos[0]
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self.vec_AC = self.mic_pos[2] - self.mic_pos[0]
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# 构建矩阵 M 及其逆矩阵 (预先计算，嵌入式可查表)
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# M = [ [AB_x, AB_y], [AC_x, AC_y] ]
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self.M = np.array([
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[self.vec_AB[0], self.vec_AB[1]],
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[self.vec_AC[0], self.vec_AC[1]]
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])
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self.M_inv = np.linalg.inv(self.M)
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# 预计算麦克风对向量 (用于快速 TDOA 理论计算)
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# 对：AB, AC, BC，index 对应 (0,1), (0,2), (1,2)
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self.pairs = [(0, 1), (0, 2), (1, 2)]
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self.pair_vecs = []
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for i, j in self.pairs:
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vec = self.mic_pos[j] - self.mic_pos[i]
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self.pair_vecs.append(vec)
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self.mic_signals = [] # 存储每次仿真生成的麦克风信号
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def generate_mic_signals(self, src_sig, true_angle):
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"""生成带延迟的信号，添加微小随机误差模拟实际情况"""
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mics = []
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theta = np.deg2rad(true_angle)
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wave_dir = np.array([-np.sin(theta), -np.cos(theta)])
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for pos in self.mic_pos:
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dist = np.dot(pos, wave_dir)
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# 添加采样噪声，模拟实际情况中的量化误差
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noised_sig = src_sig + np.random.normal(0.0, 0.01, size=len(src_sig)) # 添加微小噪声，表示采样的量化误差
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# delay = dist / self.c + random.uniform(-0.05, 0.05) * (1/self.fs) # 添加微小随机误差，范围为 ±0.05 个采样周期
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delay = dist / self.c
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mics.append(self.apply_delay_freq_domain(noised_sig, delay))
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self.mic_signals.append(mics)
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def generate_source_signal(self, duration=0.1):
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"""生成类人声宽带信号，先在频域生成所选带宽的信号，转为时域后添加噪声"""
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N = int(self.fs * duration)
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if N <= 0:
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raise ValueError("duration 必须大于 0")
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# 1) 在频域构造 200~4000Hz 的随机宽带信号（仅正频率，便于 irfft 重建实信号）
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freqs = np.fft.rfftfreq(N, d=1 / self.fs)
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spectrum = np.zeros(len(freqs), dtype=np.complex128)
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band_mask = (freqs >= 200.0) & (freqs <= 4000.0)
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band_count = np.count_nonzero(band_mask)
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if band_count == 0:
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raise ValueError("duration 过短，无法覆盖 200~4000Hz 频带")
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rand_amp = np.random.uniform(0.2, 1.0, size=band_count)
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rand_phase = np.random.uniform(0.0, 2 * np.pi, size=band_count)
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spectrum[band_mask] = rand_amp * np.exp(1j * rand_phase)
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# 2) IFFT 到时域
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signal = np.fft.irfft(spectrum, n=N)
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# 3) 添加噪声（高斯白噪声）
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noise_std = 0.05 * np.std(signal)
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if noise_std < 1e-8:
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noise_std = 1e-3
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noise = np.random.normal(0.0, noise_std, size=N)
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signal = signal + noise
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# 4) 归一化，避免后续处理溢出
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peak = np.max(np.abs(signal))
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if peak > 1e-12:
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signal = signal / peak
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return signal
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def apply_delay_freq_domain(self, signal, delay_sec):
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"""
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在频域施加分数延迟 (比时域移位更精确，模拟真实物理延迟)
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"""
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N = len(signal)
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sig_fft = fft(signal)
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# 相位偏移：e^(-j * 2 * pi * f * tau)
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freqs = np.fft.fftfreq(N, 1/self.fs) # 频率轴
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phase_shift = np.exp(-1j * 2 * np.pi * freqs * delay_sec)
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delayed_sig = ifft(sig_fft * phase_shift).real
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return delayed_sig
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def get_theoretical_tdoa(self, azimuth_deg):
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"""
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根据声源方位角计算理论 TDOA
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:param azimuth_deg: 0 度为正前方 (Mic A 方向)，顺时针增加
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"""
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theta = np.deg2rad(azimuth_deg)
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# 远场平面波单位向量 (声源传播方向，与方位角相反)
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# 如果声源在 0 度 (前方)，波向量指向 -Y 轴 (0, -1)
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# 这里定义：0 度 = +Y 轴方向来的波
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source_vec = np.array([np.sin(theta), np.cos(theta)])
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tdoas = []
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for vec in self.pair_vecs:
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# 投影距离 / 声速
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dist_diff = np.dot(vec, source_vec)
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tdoas.append(dist_diff / self.c)
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return tdoas
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def gcc_phat(self, sig1, sig2):
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"""
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核心算法：GCC-PHAT
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返回：互相关函数，峰值位置 (样本数)
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"""
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N = len(sig1)
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# 1. FFT
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S1 = fft(sig1, n=N)
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S2 = fft(sig2, n=N)
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# 2. 互功率谱
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R = S1 * np.conj(S2) # 互功率谱，乘以第二个信号的共轭，得到相位差信息
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# 3. PHAT 加权 (幅度归一化)
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# 添加 epsilon 防止除零
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epsilon = 1e-10
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R_phat = R / (np.abs(R) + epsilon)
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# 4. IFFT 得到互相关
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corr = ifft(R_phat).real # 互相关函数，长度为 N，中心点对应零延迟，左右分别对应正负延迟
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# 互相关的延迟意味着峰值位置，峰值越高说明两个信号越相似，峰值位置对应的延迟就是 TDOA 的估计值
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# 5. 峰值检测 (整数部分)
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peak_idx = np.argmax(corr)
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# 6. 抛物线插值 (分数延迟估计，关键步骤)
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# 取峰值及其左右两点拟合抛物线
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if peak_idx == 0:
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idx_prev, idx_curr, idx_next = N-1, 0, 1
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elif peak_idx == N-1:
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idx_prev, idx_curr, idx_next = N-2, N-1, 0
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else:
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idx_prev, idx_curr, idx_next = peak_idx-1, peak_idx, peak_idx+1
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y_prev, y_curr, y_next = corr[idx_prev], corr[idx_curr], corr[idx_next]
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# 抛物线顶点偏移公式
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denom = (y_prev - 2*y_curr + y_next)
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if abs(denom) < 1e-10:
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delta = 0
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else:
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delta = 0.5 * (y_prev - y_next) / denom
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fine_peak_idx = peak_idx + delta
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# 处理循环移位 (IFFT 结果后半部分代表负延迟)
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if fine_peak_idx > N / 2:
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fine_peak_idx -= N
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return corr, fine_peak_idx
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def estimate_doa_linear(self, signals):
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"""
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基于三角函数/线性方程组的直接解算方法
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速度极快，适合嵌入式
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"""
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# 1. 获取 TDOA (秒)
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_, tau_AB = self.gcc_phat(signals[0], signals[1])
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_, tau_AC = self.gcc_phat(signals[0], signals[2])
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# 2. 构建方程右侧向量 b = [c * tau_AB, c * tau_AC]
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b = np.array([self.c * tau_AB, self.c * tau_AC])
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# 3. 求解 k = M_inv * b
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# k 代表声波传播向量 [kx, ky]
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k = np.dot(self.M_inv, b)
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# 4. 归一化 (消除噪声导致的模长误差)
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norm = np.linalg.norm(k)
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if norm < 1e-6:
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return 0.0 # 避免除零
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k_hat = k / norm
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# 5. 反解角度
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# k_hat = [-sin(theta), -cos(theta)]
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theta_rad = np.arctan2(k_hat[0], k_hat[1])
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theta_deg = np.rad2deg(theta_rad)
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# 转换为 0-360
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if theta_deg < 0:
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theta_deg += 360
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return theta_deg
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def estimate_doa_grid(self, signals):
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"""
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基于网格搜索的最小二乘法 (之前版本)
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精度高，但计算量大
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"""
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measured_tdoas = []
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for i, j in self.pairs:
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_, tau = self.gcc_phat(signals[i], signals[j])
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measured_tdoas.append(tau)
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angles = np.arange(0, 360, 0.1)
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errors = []
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for ang in angles:
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theta = np.deg2rad(ang)
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source_vec = np.array([np.sin(theta), np.cos(theta)])
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err = 0
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for idx, (i, j) in enumerate(self.pairs):
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vec = self.mic_pos[j] - self.mic_pos[i]
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theo_tdoa = np.dot(vec, source_vec) / self.c
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err += (measured_tdoas[idx] - theo_tdoa)**2
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errors.append(err)
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return angles[np.argmin(errors)]
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def run_simulation(self, true_angle, sim_sig, method='linear'):
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# 解算
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if method == 'linear':
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est_angle = self.estimate_doa_linear(sim_sig)
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else:
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est_angle = self.estimate_doa_grid(sim_sig)
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return true_angle, est_angle
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# ==========================================
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# 主程序：对比两种算法
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# ==========================================
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if __name__ == "__main__":
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sim = MicArraySimulator(fs=24000, mic_dist=0.05)
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test_angles = np.arange(0, 360, 30)
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results_linear = []
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results_grid = []
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# 生成一个固定的源信号，所有测试使用同一信号，确保对比公平
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src_sig = sim.generate_source_signal(duration=0.03)
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# 生成每次仿真中ABC麦克风的信号，添加微小随机误差模拟实际情况
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for ang in test_angles:
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sim.generate_mic_signals(src_sig, ang)
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# 计时对比
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t_start = time.perf_counter()
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for i in range(len(test_angles)):
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true_a, est_a = sim.run_simulation(test_angles[i], sim.mic_signals[i], method='linear')
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results_linear.append((true_a, est_a))
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t_linear = time.perf_counter() - t_start
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t_start = time.perf_counter()
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for i in range(len(test_angles)):
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true_a, est_a = sim.run_simulation(test_angles[i], sim.mic_signals[i], method='grid')
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results_grid.append((true_a, est_a))
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t_grid = time.perf_counter() - t_start
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# 打印结果
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print(f"{'Angle':<6} | {'Linear Err':<10} | {'Grid Err':<10}")
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print("-" * 35)
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for i, ang in enumerate(test_angles):
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err_l = abs(results_linear[i][1] - results_linear[i][0])
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err_g = abs(results_grid[i][1] - results_grid[i][0])
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if err_l > 180: err_l = 360 - err_l
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if err_g > 180: err_g = 360 - err_g
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print(f"{ang:<6.0f} | {err_l:<10.2f} | {err_g:<10.2f}")
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print(f"\n耗时对比 ( {len(test_angles)} 次解算 ):")
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print(f"线性解算法：{t_linear*1000:.2f} ms")
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print(f"网格搜索法：{t_grid*1000:.2f} ms")
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print(f"速度提升：{t_grid/t_linear:.1f} 倍")
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# 可视化
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# 1. 误差分布图
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plt.figure(figsize=(10, 5))
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# 误差分布
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plt.subplot(1, 2, 1)
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errs_l = [abs(r[1]-r[0]) if abs(r[1]-r[0])<=180 else 360-abs(r[1]-r[0]) for r in results_linear]
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errs_g = [abs(r[1]-r[0]) if abs(r[1]-r[0])<=180 else 360-abs(r[1]-r[0]) for r in results_grid]
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plt.plot(test_angles, errs_l, 'b-o', label='Linear Trig')
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plt.plot(test_angles, errs_g, 'r--s', label='Grid Search')
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plt.title("DOA Estimation Error vs Angle")
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plt.xlabel("True Angle (deg)")
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plt.ylabel("Absolute Error (deg)")
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plt.legend()
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plt.grid(True, alpha=0.3)
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# 散点图
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plt.subplot(1, 2, 2)
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est_l = [r[1] for r in results_linear]
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est_g = [r[1] for r in results_grid]
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plt.plot(test_angles, test_angles, 'k--', label='Ideal')
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plt.scatter(test_angles, est_l, c='blue', label='Linear', alpha=0.6)
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plt.scatter(test_angles, est_g, c='red', marker='x', label='Grid', alpha=0.6)
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plt.title("Estimated vs True Angle")
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plt.xlabel("True Angle")
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plt.ylabel("Estimated Angle")
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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# 2. 采样波形图
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plt.figure(figsize=(12, 4))
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# 这里我们可以展示一个信号的原始波形和带延迟的波形，看看噪声的影响
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plt.subplot(1, 2, 1)
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plt.plot(src_sig, label='Original Signal')
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plt.plot(sim.mic_signals[0][0], label='Noisy&Delayed Signal')
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plt.title("Effect of Sampling Noise")
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plt.xlabel("Sample Index")
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plt.ylabel("Amplitude")
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plt.legend()
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plt.grid(True, alpha=0.3)
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# 这里展示互相关函数，看看噪声对峰值的影响
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plt.subplot(1, 2, 2)
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corr, fine_peak_idx = sim.gcc_phat(sim.mic_signals[0][0], sim.mic_signals[0][1])
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plt.plot(corr, label='GCC-PHAT Correlation')
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plt.title("GCC-PHAT Correlation")
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plt.xlabel("Lag")
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plt.ylabel("Correlation")
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.show()
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