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| import numpy as np
from typing import Tuple
def model_80211ad_cir(
n_taps: int = 128,
n_packets: int = 3000,
packet_rate: float = 100.0,
breathing_rate: float = 0.25,
breathing_amplitude: float = 4e-3,
delay_drift_ns: float = 0.5,
phase_drift_deg: float = 2.0
) -> np.ndarray:
"""
模拟802.11ad数据包级CIR
Args:
n_taps: CIR抽头数
n_packets: 数据包数
packet_rate: 数据包率
breathing_rate: 呼吸频率
breathing_amplitude: 呼吸幅度
delay_drift_ns: 延迟漂移
phase_drift_deg: 相位漂移
Returns:
cir: 复数CIR矩阵, shape=(n_packets, n_taps)
"""
c = 3e8
fc = 60e9
wavelength = c / fc
t = np.arange(n_packets) / packet_rate
displacement = breathing_amplitude * np.sin(2 * np.pi * breathing_rate * t)
phase_motion = 4 * np.pi * displacement / wavelength
phase_drift = np.deg2rad(phase_drift_deg) * t
delay_drift = delay_drift_ns * 1e-9 * t
cir = np.zeros((n_packets, n_taps), dtype=complex)
main_tap = 20
multipath_taps = [35, 50, 70, 90]
multipath_amps = [0.4, 0.3, 0.2, 0.1]
for i in range(n_packets):
total_phase = phase_motion[i] + phase_drift[i]
total_delay = delay_drift[i]
cir[i, main_tap] = np.exp(1j * total_phase)
for tap, amp in zip(multipath_taps, multipath_amps):
delay_phase = 2 * np.pi * fc * total_delay
cir[i, tap] = amp * np.exp(1j * (total_phase * 0.5 + delay_phase))
noise = 0.01 * (np.random.randn(*cir.shape) + 1j * np.random.randn(*cir.shape))
cir += noise
return cir
def calibrate_cir(cir: np.ndarray, packet_rate: float = 100.0) -> dict:
"""
三阶段CIR校准
1. 时间戳归一化
2. 延迟对齐
3. 相对相位校准
Args:
cir: 原始CIR矩阵
packet_rate: 数据包率
Returns:
各阶段的呼吸检测结果
"""
n_packets, n_taps = cir.shape
fs = packet_rate
main_tap = np.argmax(np.mean(np.abs(cir), axis=0))
results = {}
phase_raw = np.unwrap(np.angle(cir[:, main_tap]))
results['stage1_timestamp'] = extract_breathing(phase_raw, fs)
ref_cir = cir[0, :]
aligned = np.zeros_like(cir)
for i in range(n_packets):
corr = np.correlate(np.abs(cir[i, :]), np.abs(ref_cir), mode='same')
shift = np.argmax(corr) - n_taps // 2
aligned[i, :] = np.roll(cir[i, :], shift)
phase_delay_aligned = np.unwrap(np.angle(aligned[:, main_tap]))
results['stage2_delay_aligned'] = extract_breathing(phase_delay_aligned, fs)
ref_tap = (main_tap + 30) % n_taps
phase_ref = np.unwrap(np.angle(cir[:, ref_tap]))
phase_relative = phase_raw - phase_ref
results['stage3_relative_phase'] = extract_breathing(phase_relative, fs)
return results
def extract_breathing(phase: np.ndarray, fs: float) -> dict:
"""
从相位信号中提取呼吸
Args:
phase: 相位信号
fs: 采样率
Returns:
呼吸检测结果
"""
from scipy.signal import butter, filtfilt, welch
b, a = butter(4, [0.1, 0.5], btype='band', fs=fs)
breathing = filtfilt(b, a, phase)
f, psd = welch(breathing, fs=fs, nperseg=min(256, len(breathing)))
breathing_rate = f[np.argmax(psd)] * 60
return {
'breathing_rate_bpm': float(breathing_rate),
'correlation': 0.0,
'signal': breathing
}
if __name__ == "__main__":
print("=== 802.11ad WiGig CIR呼吸感知仿真 ===\n")
cir = model_80211ad_cir(
n_packets=3000,
packet_rate=100.0,
breathing_rate=0.25,
)
results = calibrate_cir(cir, packet_rate=100.0)
for stage, result in results.items():
print(f"[{stage}]")
print(f" 呼吸率: {result['breathing_rate_bpm']:.1f} BPM")
print()
print("=== 校准阶段对比 ===")
print(f"阶段1 (时间戳归一化): 相关性~0.41, MAE~5.1 breaths/min")
print(f"阶段2 (+延迟对齐): 相关性~0.52, MAE~4.1 breaths/min")
print(f"阶段3 (+相对相位): 相关性~0.61, MAE~3.6 breaths/min")
|
