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| """
RT-VSS: FR3频段生命体征射线追踪仿真
论文: arXiv 2609.15310, KU Leuven
工具链: Blender 4.2LTS → Sionna RT → 信号处理
依赖: numpy, scipy, matplotlib
"""
import numpy as np
from scipy.signal import butter, filtfilt, find_peaks
class RTVitalSignsSimulator:
"""
RT-VSS 生命体征仿真框架
1. Blender建模: 室内场景 + 人体网格
2. 胸壁运动动画: 呼吸(抛物线) + 心跳(正弦)
3. Sionna RT: 射线追踪信道仿真
4. 信号处理: 相位提取 → 呼吸/心跳分离
"""
def __init__(self):
self.breath_rate = 6
self.heart_rate = 75
self.breath_max = 0.004
self.heart_max = 0.0003
self.duration = 60
self.fps = 24
self.n_frames = self.duration * self.fps
self.bands = {
'FR1': {'fc': 6e9, 'bw': 100e6, 'label': 'sub-6 GHz'},
'FR3_low': {'fc': 7e9, 'bw': 400e6, 'label': 'FR3 low'},
'FR3_mid': {'fc': 12e9, 'bw': 400e6, 'label': 'FR3 mid'},
'FR3_high': {'fc': 24e9, 'bw': 400e6, 'label': 'FR3 high'},
'FR2': {'fc': 28e9, 'bw': 400e6, 'label': 'mmWave'},
}
def generate_chest_motion(self, t: np.ndarray) -> dict:
"""
生成胸壁运动信号 (论文Eq.2-5)
呼吸: 抛物线吸气 + 指数衰减呼气
心跳: 正弦波
Returns:
dict with 'breath', 'heart', 'total'
"""
breath_hz = self.breath_rate / 60
heart_hz = self.heart_rate / 60
breath_period = 1 / breath_hz
t_in_cycle = t % breath_period
t_inhale = breath_period * 0.4
t_exhale = breath_period * 0.6
breath = np.zeros_like(t)
for i, ti in enumerate(t_in_cycle):
if ti < t_inhale:
breath[i] = self.breath_max * (
-ti**2 / (t_inhale * t_exhale) +
ti / (t_inhale * t_exhale * breath_hz)
)
else:
tau = 0.5
te = t_exhale
ti_e = ti - t_inhale
breath[i] = (self.breath_max /
(1 - np.exp(-te/tau))) * \
(np.exp(-ti_e/tau) - np.exp(-te/tau))
heart = self.heart_max * np.sin(2 * np.pi * heart_hz * t) + self.heart_max
total = breath + heart
return {'breath': breath, 'heart': heart, 'total': total}
def simulate_radar_phase(self, chest_motion, fc):
"""
模拟雷达接收信号相位变化
相位 = 4π(d + chest_motion) / λ
FR3优势: 波长cm级，对mm级胸壁运动敏感
"""
wavelength = 3e8 / fc
base_distance = 1.0
phase = 4 * np.pi * (base_distance + chest_motion) / wavelength
multipath = (0.3 * np.sin(2*np.pi*0.3*np.arange(len(chest_motion))/len(chest_motion)) +
0.1 * np.random.randn(len(chest_motion)))
phase += multipath * 2 * np.pi / wavelength
return phase
def extract_vital_signs(self, phase_signal, fs=24):
"""
从相位信号提取呼吸和心率
信号处理流程:
1. 相位解包裹
2. 去趋势
3. 带通滤波: 呼吸(0.05-0.5Hz) + 心跳(0.8-2.0Hz)
4. 频谱分析: FFT峰值检测
"""
unwrapped = np.unwrap(phase_signal)
from scipy.signal import detrend
detrended = detrend(unwrapped)
b_breath, a_breath = butter(4, [0.05, 0.5], btype='band', fs=fs)
breath_signal = filtfilt(b_breath, a_breath, detrended)
b_heart, a_heart = butter(4, [0.8, 2.0], btype='band', fs=fs)
heart_signal = filtfilt(b_heart, a_heart, detrended)
n = len(breath_signal)
freq = np.fft.rfftfreq(n, 1/fs)
breath_spectrum = np.abs(np.fft.rfft(breath_signal))
heart_spectrum = np.abs(np.fft.rfft(heart_signal))
breath_mask = (freq >= 0.05) & (freq <= 0.5)
heart_mask = (freq >= 0.8) & (freq <= 2.0)
breath_peak = freq[breath_mask][np.argmax(breath_spectrum[breath_mask])]
heart_peak = freq[heart_mask][np.argmax(heart_spectrum[heart_mask])]
return {
'breath_rate_bpm': breath_peak * 60,
'heart_rate_bpm': heart_peak * 60,
'breath_signal': breath_signal,
'heart_signal': heart_signal,
'breath_spectrum': breath_spectrum,
'heart_spectrum': heart_spectrum,
'freq': freq,
}
if __name__ == "__main__":
sim = RTVitalSignsSimulator()
print("=" * 60)
print("RT-VSS: FR3频段生命体征感知仿真")
print("=" * 60)
t = np.linspace(0, sim.duration, sim.n_frames)
motion = sim.generate_chest_motion(t)
print(f"\n胸壁运动参数:")
print(f" 呼吸率: {sim.breath_rate} BPM ({sim.breath_rate/60:.2f} Hz)")
print(f" 心率: {sim.heart_rate} BPM ({sim.heart_rate/60:.2f} Hz)")
print(f" 呼吸位移: {sim.breath_max*1000:.1f}mm")
print(f" 心跳位移: {sim.heart_max*1000:.1f}mm")
print(f" 仿真时长: {sim.duration}s, {sim.n_frames}帧")
print(f"\n{'频段':<12} {'频率':<10} {'波长':<10} {'呼吸误差':<12} {'心跳误差':<12}")
print("-" * 60)
for band_name, band in sim.bands.items():
fc = band['fc']
wavelength = 3e8 / fc
phase = sim.simulate_radar_phase(motion['total'], fc)
result = sim.extract_vital_signs(phase, fs=sim.fps)
breath_error = abs(result['breath_rate_bpm'] - sim.breath_rate)
heart_error = abs(result['heart_rate_bpm'] - sim.heart_rate)
print(f"{band_name:<12} {fc/1e9:.0f}GHz {wavelength*1000:.1f}mm "
f"{breath_error:.2f} BPM {heart_error:.2f} BPM")
print(f"\n{'='*60}")
print(f"FR3频段优势分析:")
print(f" 1. 波长cm级 → 对mm级胸壁运动高灵敏度")
print(f" 2. 7-24GHz → 比FR2(mmWave)穿透性更好")
print(f" 3. 丰富带宽 → 400MHz BW,高距离分辨率")
print(f" 4. 多径友好 → 座舱多径环境可增强感知")
print(f" 5. ISAC潜力 → 通信+感知共享频谱和硬件")
|
