Abstract
Existing radars for stationary human detection need to process echo data from all channels simultaneously, which requires the radar system with high performance. Another limitation of existing technology is poor positioning due to sidelobe interference. Here, a time-division ultra-wideband multiple-input multiple-output (MIMO) radar is presented for stationary human detection using the incoherence of respiratory echoes. Based on the delay-and-summation imaging method, the maximum coherence factor (MCF) is weighted to suppress sidelobe, band-pass incoherence factor is weighted to distinguish stationary objects and human targets, and the variance factor is weighted to achieve accurate positioning. Experimental results show that this method can effectively suppress the noise, filter off the echo of the stationary object, and provide the distance and azimuth information of hidden human targets accurately.
Published Version
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