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| """
UWB CPD 检测算法框架
基于 Calterah Dubhe 芯片的 UWB 信号处理
"""
import numpy as np
from scipy.signal import find_peaks, butter, filtfilt
class UWBCPDDetector:
"""
UWB 儿童遗留检测器
工作原理：
1. 利用 UWB 脉冲雷达的测距能力
2. 在座舱内建立距离-时间图
3. 检测静止目标的微动（呼吸）
4. 通过 RCS/距离判断是否为儿童
"""
def __init__(self, config: dict):
self.sample_rate = config.get('sample_rate', 1e9)
self.bandwidth = config.get('bandwidth', 500e6)
self.num_samples = config.get('num_samples', 256)
self.frame_rate = config.get('frame_rate', 10)
self.range_resolution = 3e8 / (2 * self.bandwidth)
self.max_range = self.num_samples * self.range_resolution / 2
self.breath_min = 0.1
self.breath_max = 0.5
def range_profile(self, rx_signal: np.ndarray) -> np.ndarray:
"""
从接收信号提取距离剖面
Args:
rx_signal: UWB 接收信号, shape=(num_samples,)
Returns:
range_mag: 距离剖面幅度
"""
range_fft = np.fft.fft(rx_signal)
range_mag = np.abs(range_fft)
return range_mag
def detect_presence(self, range_profiles: np.ndarray,
threshold: float = 1e3) -> list:
"""
从多帧距离剖面检测目标
Args:
range_profiles: shape=(num_frames, num_samples)
Returns:
detections: [(range_bin, range_m)]
"""
avg_profile = np.mean(range_profiles, axis=0)
peaks, _ = find_peaks(avg_profile, height=threshold)
detections = []
for peak in peaks:
range_m = peak * self.range_resolution
if 0.2 < range_m < 3.0:
detections.append((peak, range_m))
return detections
def detect_micro_motion(self, range_profiles: np.ndarray,
range_bin: int) -> dict:
"""
在指定距离单元检测微动（呼吸）
Args:
range_profiles: shape=(num_frames, num_samples)
range_bin: 目标距离单元
Returns:
{'has_breathing': bool, 'breath_rate': float}
"""
phase_series = np.angle(
np.fft.fft(range_profiles, axis=1)[:, range_bin]
)
phase_series = np.unwrap(phase_series)
phase_series = phase_series - np.mean(phase_series)
nyq = self.frame_rate / 2
b, a = butter(4,
[self.breath_min/nyq, self.breath_max/nyq],
btype='band')
breath_signal = filtfilt(b, a, phase_series)
spectrum = np.abs(np.fft.fft(breath_signal))
freqs = np.fft.fftfreq(len(breath_signal), 1/self.frame_rate)
pos_mask = freqs > 0
breath_mask = (freqs[pos_mask] >= self.breath_min) & \
(freqs[pos_mask] <= self.breath_max)
if np.any(breath_mask):
peak_idx = np.argmax(spectrum[pos_mask][breath_mask])
breath_freq = freqs[pos_mask][breath_mask][peak_idx]
breath_rate = breath_freq * 60
return {'has_breathing': True, 'breath_rate': breath_rate}
return {'has_breathing': False, 'breath_rate': 0.0}
def classify_target(self, range_m: float, breath_rate: float,
rcs_estimate: float) -> str:
"""
分类目标类型
Returns:
'child' | 'adult' | 'pet' | 'object' | 'unknown'
"""
if breath_rate == 0:
return 'object'
if 0.5 <= breath_rate <= 1.0:
return 'child'
elif 0.33 <= breath_rate < 0.5:
if rcs_estimate < 0.01:
return 'child'
return 'adult'
elif 0.2 <= breath_rate < 0.33:
return 'adult'
else:
return 'unknown'
if __name__ == "__main__":
detector = UWBCPDDetector({
'sample_rate': 1e9,
'bandwidth': 500e6,
'num_samples': 256,
'frame_rate': 10
})
print(f"距离分辨率: {detector.range_resolution*100:.1f} cm")
print(f"最大距离: {detector.max_range:.1f} m")
np.random.seed(42)
profiles = np.random.randn(100, 256) * 0.1 + 0.5
for i in range(100):
profiles[i, 50] += np.sin(2 * np.pi * 0.3 * i / 10)
dets = detector.detect_presence(profiles)
for bin_idx, range_m in dets:
motion = detector.detect_micro_motion(profiles, bin_idx)
if motion['has_breathing']:
target = detector.classify_target(
range_m, motion['breath_rate'], 0.005
)
print(f"目标: 距离={range_m:.2f}m, "
f"呼吸={motion['breath_rate']:.1f}次/分, "
f"类型={target}")
|