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LIU Changyu, ZHANG Hao, GENG Fanglin, et al. Dynamic demodulation algorithm for bio-radar sensors based on range tapper[J]. Journal of Radars, in press. doi: 10.12000/JR24151
Citation: LIU Changyu, ZHANG Hao, GENG Fanglin, et al. Dynamic demodulation algorithm for bio-radar sensors based on range tapper[J]. Journal of Radars, in press. doi: 10.12000/JR24151
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Dynamic Demodulation Algorithm for Bio-radar Sensors Based on Range Tapper

DOI: 10.12000/JR24151
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • 2.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049, China

  • 3.

    Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing 100094, China

  • 4.

    School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University Shanghai 200030, China

Funds:  The National Natural Science Foundation of China (62331025, U21A20447, 62071451), National Key Research and Development Project (2021YFC3002204), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-019)
More Information
  • Corresponding author: FANG Zhen, zfang@mail.ie.ac.cn
  • Received Date: 2024-07-23
  • Rev Recd Date: 2024-10-08
    • Available Online: 2024-10-20
    Abstract
  • In non-inductive radar vital sign monitoring, frequency-modulated radars (such as Frequency Modulated Continuous Wave (FMCW) and Ultra WideBand (UWB)) are more effective than Continuous Wave (CW) radars at distinguishing targets from clutter in terms of distance. Using range Fourier transform, the heartbeat and breathing signals can be extracted from quasi-static targets across various distance intervals, thereby improving monitoring accuracy. However, the commonly used range Fast Fourier Transform (FFT) presents certain limitations: The breathing amplitude of the subject may cross the range bin boundary, compromising signal integrity, while breathing movements can cause amplitude modulation of physiological signals, hindering waveform recovery. To address these reasons, we propose an algorithm architecture featuring range tap reconstruction and dynamic demodulation. We tested the algorithm performance in simulations and experiments for the cross range bin cases. Simulation results indicate that processing signals crossing range bins with our algorithm improves the signal-to-noise ratio by 17±5 dB. In addition, experiments recorded Doppler Heartbeat Diagram (DHD) signals from eight subjects, comparing the consistency between the DHD signals and the ballistocardiogram. The root means square error of the C-C interval in the DHD signal relative to the J-J interval in the BallistoCardioGram (BCG) signal was 21.58±13.26 ms (3.40%±2.08%).

     

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