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The use of millimeter wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may shake or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located at various times, extracting target phase information. The noise components such as movement and shaking contained in the phase need to be removed. In this paper, MTI is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and the fluctuation of range bin selection is removed through moving average filtering. Wavelet transform is used to decompose the phase signal, remove shaking noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.
The use of millimeter wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may shake or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located at various times, extracting target phase information. The noise components such as movement and shaking contained in the phase need to be removed. In this paper, MTI is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and the fluctuation of range bin selection is removed through moving average filtering. Wavelet transform is used to decompose the phase signal, remove shaking noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.
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