| As people’s living standards continue to improve,the demand for self-health testing is also increasing.Respiration and heartbeat are important vital signs of the human body.However,the current contact breathing and heartbeat measurement cannot meet the needs in all scenarios,so non-contact measurement of breathing and heartbeat The method has been extensively studied.Among them,the millimeterwave radar has low cost and accurate target positioning.At the same time,the higher frequency ensures the accuracy of phase measurement,which is more accurate than the measurement results of optical and ultrasonic measurement equipment.At the same time,it is not interfered by light and noise,and the measurement result is more stable.Through the detection of thoracic motion,the extraction and measurement of respiratory and heartbeat signals are realized.This paper first introduces the process and method of frequency modulated continuous wave(FMCW)radar vital sign signal preprocessing,using the low-speed characteristics of vital sign information to accumulate in the Doppler domain,and simulations verify that this method can effectively improve signal-to-noise Then,using constant false alarm detection and minimum non-variance angle estimation,the distance point cloud data is obtained,and finally the density-based clustering method is used to achieve the removal of noise.After the clustering result in the test scene is obtained,the beamforming method is used to enhance the target at different angles,and it is judged whether it is a human target according to its phase information.The center estimation method is used to remove the DC component in the phase information,and the arctangent demodulation and expansion are compared.Then this paper studied the separation and frequency estimation of vital sign signals.After discussion and analysis,the differential signal is more suitable for frequency estimation.At the same time,the difference can also eliminate the largescale change trend of the vital sign signal and make the signal more stable.In the traditional filtering method,the higher harmonics of breathing will affect the heartbeat signal.Therefore,the variational modal decomposition(VMD)is used to adaptively filter out the harmonic interference,and the spectral entropy is used to determine the signal quality after decomposition.The VMD method can extract the respiratory harmonics,thereby obtaining better signal quality.In frequency estimation,short-time Fourier transform and short-time AR estimation are used to extract frequency information,and a harmonic discrimination strategy is proposed,which can improve the result of heart rate estimation.Finally,the method proposed in this paper was tested and verified in different scenarios,and the measurement results of different range,different angles,and different states of a single person,different angles and different range of two persons,and the same range and different angle measurement results,and the measurement results of a single person after exercise were verified.result.The results show that in the single-person scenario,the estimated root mean square error of heart rate within230 s is less than 7.55 bpm,and the estimated root mean square error of vital signs during exercise recovery is 10.82 bpm.The estimation error in the multiplayer scene is less than 9.89 bpm,and the vital signs of different targets can be separated at the same distance at the same angle.The respiration waveform conforms to the actual condition of the measurement target.The measurement result is more accurate in the resting state,but the relative error of the measurement result in the non-rest state is relatively large,but the change trend of the breathing and heartbeat is still consistent with the actual situation. |
