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| """
Calterah多站UWB感知仿真
单站(monostatic) vs 多站(multistatic)对比
依赖: numpy, matplotlib
"""
import numpy as np
from typing import List, Tuple
class UWBSensingSimulator:
"""
UWB座舱感知仿真器
对比:
1. 单站: 1个锚点自发自收
2. 多站: 多个锚点同步收发
场景: 座椅上有一个儿童(目标)
锚点: 4个UWB节点(前左、前右、后左、后右)
"""
def __init__(self):
self.cabin_length = 3.0
self.cabin_width = 1.8
self.cabin_height = 1.2
self.anchors = {
'front_left': np.array([0.3, 0.3, 0.8]),
'front_right': np.array([0.3, 1.5, 0.8]),
'rear_left': np.array([2.7, 0.3, 0.8]),
'rear_right': np.array([2.7, 1.5, 0.8]),
'center': np.array([1.5, 0.9, 0.6]),
}
self.target = np.array([2.0, 0.9, 0.3])
self.bandwidth = 500e6
self.center_freq = 8.5e9
self.wavelength = 3e8 / self.center_freq
def simulate_monostatic(self, anchor_name='center'):
"""
单站UWB: 一个锚点自发自收
问题:
- 只有一个视角，遮挡即失效
- 角度分辨率差
- 多径衰落敏感
"""
anchor = self.anchors[anchor_name]
distance = np.linalg.norm(self.target - anchor)
t_delay = 2 * distance / 3e8
breath_freq = 0.2
heart_freq = 1.25
breath_amp = 0.004
heart_amp = 0.0003
t = np.linspace(0, 5, 1000)
chest_motion = (breath_amp * np.sin(2*np.pi*breath_freq*t) +
heart_amp * np.sin(2*np.pi*heart_freq*t))
phase = 4 * np.pi * (distance + chest_motion) / self.wavelength
return {
'mode': 'monostatic',
'anchor': anchor_name,
'distance': distance,
'phase': phase,
'chest_motion': chest_motion,
'issues': [
'单视角，遮挡即失效',
'角度分辨率差',
'多径衰落敏感',
'近距离目标盲区'
]
}
def simulate_multistatic(self, anchor_names=None):
"""
多站UWB: 多个锚点同步收发
优势:
- 空间分集，消除遮挡
- 三角测量提高角度分辨率
- 多径去相关，衰落鲁棒
- 生命体征交叉验证
"""
if anchor_names is None:
anchor_names = list(self.anchors.keys())
anchors = [self.anchors[name] for name in anchor_names]
n_anchors = len(anchors)
distances = []
phases = []
t = np.linspace(0, 5, 1000)
breath_freq = 0.2
heart_freq = 1.25
breath_amp = 0.004
heart_amp = 0.0003
chest_motion = (breath_amp * np.sin(2*np.pi*breath_freq*t) +
heart_amp * np.sin(2*np.pi*heart_freq*t))
for i, tx in enumerate(anchors):
for j, rx in enumerate(anchors):
if i == j:
continue
d_tx = np.linalg.norm(self.target - tx)
d_rx = np.linalg.norm(self.target - rx)
d_total = d_tx + d_rx
distances.append({
'tx': anchor_names[i],
'rx': anchor_names[j],
'distance': d_total
})
phase = 2 * np.pi * (d_total + chest_motion) / self.wavelength
phases.append(phase)
fused_phase = np.mean(phases, axis=0)
from scipy.signal import butter, filtfilt
b_breath, a_breath = butter(4, [0.1, 0.5], btype='band', fs=200)
breath_signal = filtfilt(b_breath, a_breath, fused_phase)
b_heart, a_heart = butter(4, [0.8, 2.0], btype='band', fs=200)
heart_signal = filtfilt(b_heart, a_heart, fused_phase)
return {
'mode': 'multistatic',
'n_anchors': n_anchors,
'n_links': len(distances),
'distances': distances,
'fused_phase': fused_phase,
'breath_signal': breath_signal,
'heart_signal': heart_signal,
'advantages': [
f'{len(distances)}条多站链路交叉验证',
'空间分集消除遮挡',
'三角测量提高定位精度',
'多径去相关提升信噪比',
'可分离近距离目标(儿童vs成人)'
]
}
def compare_detection_accuracy(self):
"""对比单站vs多站的检测精度"""
n_trials = 100
mono_detect = 0
multi_detect = 0
for _ in range(n_trials):
obscured = np.random.choice(['none', 'left', 'right', 'front'])
if obscured == 'none':
mono_detect += 1
if obscured == 'left':
visible = ['front_right', 'rear_right']
elif obscured == 'right':
visible = ['front_left', 'rear_left']
elif obscured == 'front':
visible = ['rear_left', 'rear_right']
else:
visible = list(self.anchors.keys())
if len(visible) >= 2:
multi_detect += 1
return {
'monostatic_detection_rate': mono_detect / n_trials,
'multistatic_detection_rate': multi_detect / n_trials,
'improvement': (multi_detect - mono_detect) / n_trials
}
if __name__ == "__main__":
sim = UWBSensingSimulator()
print("=" * 60)
print("Calterah多站UWB vs 单站UWB 对比仿真")
print("=" * 60)
print(f"\nUWB锚点位置:")
for name, pos in sim.anchors.items():
print(f" {name}: {pos}")
print(f" 目标(儿童): {sim.target}")
mono = sim.simulate_monostatic('center')
print(f"\n--- 单站UWB ---")
print(f" 锚点: {mono['anchor']}")
print(f" 距离: {mono['distance']:.3f}m")
print(f" 问题:")
for issue in mono['issues']:
print(f" ❌ {issue}")
multi = sim.simulate_multistatic()
print(f"\n--- 多站UWB ---")
print(f" 锚点数: {multi['n_anchors']}")
print(f" 多站链路数: {multi['n_links']}")
print(f" 优势:")
for adv in multi['advantages']:
print(f" ✅ {adv}")
comp = sim.compare_detection_accuracy()
print(f"\n--- 蒙特卡洛检测率对比 (100次) ---")
print(f" 单站检测率: {comp['monostatic_detection_rate']:.1%}")
print(f" 多站检测率: {comp['multistatic_detection_rate']:.1%}")
print(f" 提升幅度: +{comp['improvement']:.1%}")
|
