Research On Multi-Target Vital Sign Monitoring Algorithm With Millimeter Wave Radar

In the past few decades,radar has been gradually applied in the field of medical care and disaster rescue worldwide.The non-contact detection of human vital signs based on radar system has a wide range of application prospects.Due to the characteristics of electromagnetic waves,radar can detect vital signs of targets in a non-contact manner.The application of FMC W radar in the detection of vital signs has been studied.FMCW radar has suitable range resolution and relatively simple system architecture.In addition,it can detect both absolute distance and Doppler effect.Using the unique advantage of FMCW radar,this paper studies the detection of vital signs.The main technical contents are as follows:(1)Multi-target vital sign monitoring algorithm is studied by using multiple-input multiple-output(MIMO)radar.The differentiation of multitarget vital signs requires the differentiation of Angle dimensions by multiple receiving antennas.Compared with the single-transmit and multiple-receive radar MIMO is more economical and effective,so this paper uses the MIMO system two-transmit and four-receive radar as the hardware basis for research.In this paper,the Angle measurement is carried out by MIMO radar equivalent to one shot and eight receivers,and the estimated Angle is used for digital beamforming(DBF)to point the beam to the target position and extract its information.MIMO can be used for information extraction to achieve a higher signal-to-noise ratio,no matter whether the scene is single target or multi-target.The channel calibration of the array antenna is carried out to reduce its influence on the angle estimation and phase extraction.(2)Because the conventional FFT algorithm has some spectrum leakage problem,and simulation proves that the adjacent frequency points are unstable when FFT phase extraction.So in this paper,all phase FFT(apFFT)is adopted to make up for the shortcomings of FFT.Firstly,through simulation,it is explained that random body fretting(RBM)will lead to changes in the distance peak value during phase extraction,and it is also explained that the position of the strongest reflection point is not single when the chest cavity is the surface target,which leads to changes in the distance peak value.Since the phase extraction of apFFT is stable,it can show its advantage in the face of slow time dimension peak distance element is not single.(3)In this paper,the existing problems of RBM are analyzed,and it is confirmed that RBM will cause the phenomenon of covering the spectrum of respiration and heartbeat.Through simulation,the methods of eliminating RBM in the two articles were compared,and linear fitting was used in both of them.It was found that the common problem was that it needed to manually select parameters several times,including the order of linear fitting or the length of time template.Therefore,in order to separate RBM from respiration and heartbeat,improve efficiency and reduce human interference,the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis(CEEMDAN)method is selected for frequency separation in this paper.CEEMDAN algorithm can improve mode aliasing in empirical mode decomposition(EMD).