1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
import numpy as np
from scipy import signal
class UWBCPDValidator:
"""
UWB雷达儿童检测验证器
基于needCode电子书Channel Impulse Response (CIR)分析
"""
def __init__(self, fs: int = 100, chirp_duration: float = 0.01):
"""
Args:
fs: 采样率(Hz)，典型值100Hz
chirp_duration: Chirp周期(s)，UWB典型值10ms
"""
self.fs = fs
self.chirp_duration = chirp_duration
self.breathing_band = (0.2, 0.6)
def extract_vital_signs(self, cir_data: np.ndarray) -> dict:
"""
从CIR数据提取生命体征
Args:
cir_data: Channel Impulse Response, shape=(N_samples, N_antennas)
Returns:
dict: {
'breathing_rate': 呼吸频率(Hz),
'presence_detected': 是否检测到存在,
'confidence': 检测置信度
}
"""
phase = np.angle(cir_data)
phase_unwrap = np.unwrap(phase, axis=0)
b, a = signal.butter(4, self.breathing_band, btype='band', fs=self.fs)
breathing_signal = signal.filtfilt(b, a, phase_unwrap, axis=0)
fft_result = np.fft.fft(breathing_signal, axis=0)
freqs = np.fft.fftfreq(len(breathing_signal), 1/self.fs)
positive_freqs = freqs[freqs > 0]
breathing_band_idx = (positive_freqs >= self.breathing_band[0]) & \
(positive_freqs <= self.breathing_band[1])
breathing_spectrum = np.abs(fft_result[1:len(positive_freqs)+1])
breathing_spectrum_band = breathing_spectrum[breathing_band_idx]
if len(breathing_spectrum_band) > 0:
peak_idx = np.argmax(breathing_spectrum_band)
breathing_rate = positive_freqs[breathing_band_idx][peak_idx]
confidence = breathing_spectrum_band[peak_idx] / np.mean(breathing_spectrum_band)
presence_detected = confidence > 3.0
else:
breathing_rate = 0
confidence = 0
presence_detected = False
return {
'breathing_rate': breathing_rate,
'presence_detected': presence_detected,
'confidence': confidence
}
def estimate_range_resolution(self, bandwidth_hz: float = 500e6) -> float:
"""
估算距离分辨率
Args:
bandwidth_hz: UWB带宽(Hz)，needCode典型值500MHz
Returns:
range_resolution_m: 距离分辨率(米)
Formula:
range_resolution = c / (2 * B)
c = 光速 = 3e8 m/s
"""
c = 3e8
range_resolution = c / (2 * bandwidth_hz)
return range_resolution
def test_occluded_child_detection(self):
"""
测试遮挡儿童检测能力
needCode电子书关键结论：
- UWB可穿透座椅+毛毯检测呼吸微动
- 60GHz FMCW被阻挡
"""
N_samples = 1000
N_antennas = 4
t = np.arange(N_samples) / self.fs
breathing_signal_clean = np.sin(2 * np.pi * 0.33 * t)
cir_clean = np.exp(1j * breathing_signal_clean)
attenuation_factor = 0.4
breathing_signal_occluded = attenuation_factor * np.sin(2 * np.pi * 0.33 * t)
cir_occluded = np.exp(1j * breathing_signal_occluded)
result_clean = self.extract_vital_signs(cir_clean.reshape(-1, 1))
result_occluded = self.extract_vital_signs(cir_occluded.reshape(-1, 1))
print("="*60)
print("UWB雷达儿童检测精度验证")
print("="*60)
print(f"距离分辨率: {self.estimate_range_resolution()*100:.1f} cm")
print(f"\n场景1 - 无遮挡:")
print(f" 呼吸频率: {result_clean['breathing_rate']:.3f} Hz ({result_clean['breathing_rate']*60:.1f} 次/分)")
print(f" 检测结果: {'✅ 检测到' if result_clean['presence_detected'] else '❌ 未检测到'}")
print(f" 置信度: {result_clean['confidence']:.2f}")
print(f"\n场景2 - 毛毯遮挡:")
print(f" 呼吸频率: {result_occluded['breathing_rate']:.3f} Hz ({result_occluded['breathing_rate']*60:.1f} 次/分)")
print(f" 检测结果: {'✅ 检测到' if result_occluded['presence_detected'] else '❌ 未检测到'}")
print(f" 置信度: {result_occluded['confidence']:.2f}")
print(f"\n结论: UWB在{attenuation_factor*100:.0f}%衰减下仍可检测呼吸")
if __name__ == "__main__":
validator = UWBCPDValidator(fs=100)
validator.test_occluded_child_detection()
|