SCA-CFAR Algorithm for Indoor Target Detection Based on LFMCW Millimeter-Wave Radar

The cell-averaging constant false alarm rate (CA-CFAR) detection algorithm is a crucial step in indoor target detection using linear frequency modulated continuous wave (LFMCW) millimeter-wave radar. However, due to the complexity of indoor environments, the traditional CA-CFAR algorithm is prone to target masking effects in multi-target scenarios. This paper proposes an improved algorithm, SCA-CFAR, which builds upon the CA-CFAR algorithm by dividing the reference cells on both sides into equal segments. The algorithm then calculates the average of each reference cell segment separately and compares the scaled segment averages with the overall reference cell average. The segment averages that exceed the overall reference cell average are further scaled to determine a new detection threshold. Experimental verification shows that the SCA-CFAR algorithm can effectively suppress the masking effect that occurs in target detection using the CA-CFAR algorithm, thereby improving the performance of indoor target detection with millimeter-wave radar.