Abstract
A strong interference adaptive suppression algorithm based on frequency-modulated continuous wave (FMCW) radar is proposed for scenarios with complex static targets. The algorithm removes stationary clutter, stationary fixed targets, and random background noise contained in the echo signal through background resolution, background accumulation, background differencing, and adaptive background update and detects human targets using the cell-averaged selected small constant false alarm rate (SO-CFAR) algorithm. Experiments show that this method improves target detection accuracy in low SNR, reduces the impact of background removal on the target phase, and improves the accuracy of target respiration and heartbeat value estimation.
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