Research On Non-contact Vital Signs Detection Technology Based On Frequency Modulated Continuous Wave Radar

The life signal detection technology based on radar has the advantage of long-distance non-contact detection.It has a wide application prospect in the fields of medical monitoring,home health,epidemic prevention and so on.The frequency modulated continuous wave(FMCW)radar usually has the advantages of low cost,simple system,small size,measurable target information and short distance measuring blind area,so that it plays an important role in the field of non-contact sign detection.However,there are still some problems in the research,which affect the accuracy and real-time performance of vital signs detection,such as respiratory harmonic interference,multi-target mixing and so on.In this paper,blind source separation algorithm is used to realize multi-target vital signs detection,reduce multi-target interference,add preprocessing,and variational modal decomposition algorithm is used to realize single target vital signs signal separation,alleviate respiratory harmonic interference,and improve the detection accuracy.The specific research contents are as follows:Aiming at the problem of single target respiratory harmonic interference,this paper analyzes that the radar original vital sign echo signal is vulnerable to hardware reasons and human body swing,and adopts the detrending algorithm and the algorithm based on sparse denoising and baseline estimation to remove the interference.In order to further extract the respiratory and heartbeat frequencies in the radar echo signal,the short-time Fourier transform(STFT),harmonic path algorithm(HAPA)and variational modal decomposition(VMD)algorithms are used for time-frequency analysis respectively.Through analysis and comparison,VMD algorithm has better separation effect in the process of extracting life signal,strong anti-harmonic interference ability and high accuracy.Finally,the system platform is built,and the denoising algorithm and variational modal decomposition algorithm are used to extract the single vital sign signal.The monitoring time is within1 min,and the detection accuracy is 96.3%.The experimental results show that the method used in this paper has high accuracy and good stability.Aiming at the problem of multi-target and multi-path aliasing interference,a blind source separation(BSS)signal detection method based on fast fixed point algorithm(Fast ICA)is proposed to realize multi-target vital sign signal detection.The algorithm evaluates the non-Gauss by entropy,selects the appropriate transformation matrix,and there is no statistical correlation between the target vital signs signals.According to its statistical independence,the source signal is separated from the observed mixed signals.On this basis,the traditional blind source separation processing flow is improved,and the wavelet transform preprocessing algorithm based on translation invariants is added to suppress the interference of environmental static clutter.The algorithm simulation and experiments show that the proposed algorithm can effectively extract the multi-target vital signs signals under the interference of ambient clutter,and the detection accuracy reaches 93.7%,which has high accuracy and stability.