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| import numpy as np
from dataclasses import dataclass
from typing import Optional
from scipy import signal
@dataclass
class CPDDetection:
"""儿童存在检测结果"""
detected: bool
confidence: float
vital_signs: dict
position: tuple
motion_level: float
class MillimeterWaveCPD:
"""
60GHz毫米波雷达CPD检测
硬件：TI IWR6843AOP / ARIA Hydrogen
核心原理：
1. 发射FMCW信号
2. 接收反射信号
3. 分离静态和动态分量
4. 从动态分量提取呼吸/心跳频率
"""
def __init__(
self,
sample_rate: int = 100,
fft_size: int = 2048,
breath_freq_range: tuple = (0.1, 0.5),
heart_freq_range: tuple = (1.0, 2.0)
):
self.sample_rate = sample_rate
self.fft_size = fft_size
self.breath_freq_range = breath_freq_range
self.heart_freq_range = heart_freq_range
def process_radar_data(
self,
adc_data: np.ndarray
) -> CPDDetection:
"""
处理雷达ADC数据
Args:
adc_data: 雷达ADC采样数据
Returns:
CPDDetection: 检测结果
"""
range_doppler = self._compute_range_doppler(adc_data)
range_doppler_clean = self._remove_static_clutter(range_doppler)
motion_map = self._detect_motion(range_doppler_clean)
vital_signs = self._extract_vital_signs(adc_data)
detected = (
motion_map['total_motion'] > 0.1 or
vital_signs['breath_rate'] > 0
)
return CPDDetection(
detected=detected,
confidence=self._compute_confidence(motion_map, vital_signs),
vital_signs=vital_signs,
position=self._locate_target(range_doppler_clean),
motion_level=motion_map['total_motion']
)
def _compute_range_doppler(self, adc_data: np.ndarray) -> np.ndarray:
"""计算距离-多普勒图"""
range_fft = np.fft.fft(adc_data, n=self.fft_size, axis=0)
range_doppler = np.fft.fftshift(
np.fft.fft(range_fft, axis=0),
axes=0
)
return np.abs(range_doppler)
def _remove_static_clutter(self, range_doppler: np.ndarray) -> np.ndarray:
"""静态杂波抑制（移除零多普勒分量）"""
center = range_doppler.shape[0] // 2
range_doppler[center-5:center+5] *= 0.01
return range_doppler
def _detect_motion(self, range_doppler: np.ndarray) -> dict:
"""检测运动目标"""
total_energy = np.sum(range_doppler)
motion_threshold = 0.05 * total_energy
motion_map = (range_doppler > motion_threshold).astype(float)
total_motion = np.sum(motion_map) / range_doppler.size
return {
'motion_map': motion_map,
'total_motion': total_motion
}
def _extract_vital_signs(self, adc_data: np.ndarray) -> dict:
"""
提取生命体征（呼吸率、心率）
原理：胸腔微动调制雷达回波相位
- 呼吸：胸腔起伏约1-5mm，频率0.1-0.5Hz
- 心跳：胸壁微动约0.2-0.5mm，频率1-2Hz
"""
phase = np.angle(adc_data[:, 0])
phase_unwrapped = np.unwrap(phase)
phase_detrended = signal.detrend(phase_unwrapped)
freq = np.fft.fftfreq(len(phase_detrended), 1/self.sample_rate)
spectrum = np.abs(np.fft.fft(phase_detrended))
pos_freq = freq[:len(freq)//2]
pos_spectrum = spectrum[:len(spectrum)//2]
breath_mask = (pos_freq >= self.breath_freq_range[0]) & \
(pos_freq <= self.breath_freq_range[1])
if np.any(breath_mask):
breath_peak_idx = np.argmax(pos_spectrum[breath_mask])
breath_freq = pos_freq[breath_mask][breath_peak_idx]
breath_rate = breath_freq * 60
else:
breath_rate = 0
heart_mask = (pos_freq >= self.heart_freq_range[0]) & \
(pos_freq <= self.heart_freq_range[1])
if np.any(heart_mask):
heart_peak_idx = np.argmax(pos_spectrum[heart_mask])
heart_freq = pos_freq[heart_mask][heart_peak_idx]
heart_rate = heart_freq * 60
else:
heart_rate = 0
return {
'breath_rate': breath_rate,
'heart_rate': heart_rate,
'phase_signal': phase_detrended
}
def _compute_confidence(self, motion_map: dict, vital_signs: dict) -> float:
"""计算检测置信度"""
motion_score = min(motion_map['total_motion'] / 0.5, 1.0)
breath_score = 1.0 if 10 < vital_signs['breath_rate'] < 40 else 0.5
heart_score = 1.0 if 60 < vital_signs['heart_rate'] < 160 else 0.5
confidence = 0.3 * motion_score + 0.4 * breath_score + 0.3 * heart_score
return confidence
def _locate_target(self, range_doppler: np.ndarray) -> tuple:
"""定位目标位置"""
max_idx = np.unravel_index(np.argmax(range_doppler), range_doppler.shape)
row = max_idx[0] // (range_doppler.shape[0] // 3)
seat = max_idx[1] // (range_doppler.shape[1] // 3)
return (row, seat)
if __name__ == "__main__":
cpd = MillimeterWaveCPD(sample_rate=100, fft_size=2048)
t = np.linspace(0, 5, 500)
breath_signal = 0.5 * np.sin(2 * np.pi * 0.25 * t)
heart_signal = 0.1 * np.sin(2 * np.pi * 1.2 * t)
noise = 0.05 * np.random.randn(len(t))
phase = breath_signal + heart_signal + noise
adc_data = np.exp(1j * 2 * np.pi * phase).reshape(-1, 1)
result = cpd.process_radar_data(adc_data)
print("=" * 60)
print("CPD检测结果")
print("=" * 60)
print(f"检测到儿童: {result.detected}")
print(f"置信度: {result.confidence:.2f}")
print(f"呼吸率: {result.vital_signs['breath_rate']:.1f} 次/分钟")
print(f"心率: {result.vital_signs['heart_rate']:.1f} 次/分钟")
print(f"运动水平: {result.motion_level:.3f}")
print(f"位置: 第{result.position[0]+1}排, 第{result.position[1]+1}座")
|