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| import numpy as np
from typing import Tuple
def model_chest_displacement(
breathing_rate: float = 0.25,
breathing_amplitude: float = 4e-3,
heart_rate: float = 1.2,
heart_amplitude: float = 0.5e-3,
duration_s: float = 10.0,
fs: float = 1000.0
) -> Tuple[np.ndarray, np.ndarray]:
"""
模拟胸部位移（呼吸+心跳）
参考: RT-VSS论文中的Blender生理建模
Args:
breathing_rate: 呼吸频率(Hz)
breathing_amplitude: 呼吸幅度(m), 正常4-12mm
heart_rate: 心率(Hz)
heart_amplitude: 心跳幅度(m), 正常0.2-0.5mm
duration_s: 持续时间
fs: 采样率
Returns:
(t, displacement): 时间轴和胸部位移
"""
t = np.linspace(0, duration_s, int(duration_s * fs))
breathing = breathing_amplitude * np.sin(2 * np.pi * breathing_rate * t)
heartbeat = heart_amplitude * (
np.sin(2 * np.pi * heart_rate * t) +
0.3 * np.sin(4 * np.pi * heart_rate * t)
)
displacement = breathing + heartbeat
return t, displacement
def simulate_fr3_radar_sensing(
displacement: np.ndarray,
carrier_freq_ghz: float = 10.0,
bandwidth_ghz: float = 2.0,
n_rx_positions: int = 10,
fs: float = 1000.0
) -> dict:
"""
模拟FR3频段雷达感知
Args:
displacement: 胸部位移
carrier_freq_ghz: 载波频率
bandwidth_ghz: 带宽
n_rx_positions: 接收位置数
fs: 采样率
Returns:
检测结果
"""
c = 3e8
wavelength = c / (carrier_freq_ghz * 1e9)
phase_change = 4 * np.pi * displacement / wavelength
results = []
for rx_idx in range(n_rx_positions):
multipath_delay = np.random.uniform(0, 50e-9, 5)
multipath_amp = np.random.uniform(0.1, 0.5, 5)
rx_signal = np.exp(1j * phase_change)
for d, a in zip(multipath_delay, multipath_amp):
rx_signal += a * np.exp(1j * (phase_change + 2 * np.pi * carrier_freq_ghz * 1e9 * d))
phase_extracted = np.unwrap(np.angle(rx_signal))
from scipy.signal import butter, filtfilt
b_resp, a_resp = butter(4, [0.1, 0.5], btype='band', fs=fs)
breathing_signal = filtfilt(b_resp, a_resp, phase_extracted)
b_heart, a_heart = butter(4, [0.8, 2.0], btype='band', fs=fs)
heart_signal = filtfilt(b_heart, a_heart, phase_extracted)
from scipy.signal import welch
f_resp, psd_resp = welch(breathing_signal, fs=fs, nperseg=1024)
f_heart, psd_heart = welch(heart_signal, fs=fs, nperseg=1024)
breathing_rate_est = f_resp[np.argmax(psd_resp)] * 60
heart_rate_est = f_heart[np.argmax(psd_heart)] * 60
results.append({
'rx_position': rx_idx,
'breathing_rate_bpm': breathing_rate_est,
'heart_rate_bpm': heart_rate_est,
'snr_db': 10 * np.log10(np.max(psd_resp) / np.mean(psd_resp))
})
return {
'carrier_freq_ghz': carrier_freq_ghz,
'wavelength_mm': wavelength * 1000,
'results': results
}
if __name__ == "__main__":
print("=== RT-VSS FR3频段生命体征感知仿真 ===\n")
t, disp = model_chest_displacement(
breathing_rate=0.25,
heart_rate=1.2,
duration_s=10.0
)
print(f"呼吸幅度: {np.max(disp)*1000:.2f} mm")
print(f"载波波长(FR3@10GHz): {3e8/10e9*1000:.1f} mm")
print(f"相位变化范围: ±{4*np.pi*np.max(disp)/(3e8/10e9):.4f} rad")
print("\n[FR3 - 10 GHz]")
result_fr3 = simulate_fr3_radar_sensing(disp, carrier_freq_ghz=10.0)
for r in result_fr3['results'][:3]:
print(f" RX{r['rx_position']}: 呼吸={r['breathing_rate_bpm']:.1f} BPM, "
f"心率={r['heart_rate_bpm']:.1f} BPM, SNR={r['snr_db']:.1f} dB")
print("\n[FR1 - 3.5 GHz]")
result_fr1 = simulate_fr3_radar_sensing(disp, carrier_freq_ghz=3.5)
for r in result_fr1['results'][:3]:
print(f" RX{r['rx_position']}: 呼吸={r['breathing_rate_bpm']:.1f} BPM, "
f"心率={r['heart_rate_bpm']:.1f} BPM, SNR={r['snr_db']:.1f} dB")
print("\n[FR2 - 28 GHz]")
result_fr2 = simulate_fr3_radar_sensing(disp, carrier_freq_ghz=28.0)
for r in result_fr2['results'][:3]:
print(f" RX{r['rx_position']}: 呼吸={r['breathing_rate_bpm']:.1f} BPM, "
f"心率={r['heart_rate_bpm']:.1f} BPM, SNR={r['snr_db']:.1f} dB")
|