Area-Based CFAR Target Detection for Automotive Millimeter-Wave Radar

Millimeter-wave (mmWave) radar is critical to the emerging automatous driving. As one important step to estimate the range/velocity of unknown targets, constant false-alarm rate (CFAR) techniques should be firstly applied. Most CFAR methods focus on the point -based target model (i.e. each target is represented as one point), which may be inadequate for the highly accurate detection scenarios. Owing to the largely improved temporal/spatial resolutions of mmWave radars, each target is now dispersed to many reflection points, covered by a certain area on Range Doppler Map (RDM). In this work, we fully utilize such new information provided by mmWave radars, and develop an area -based CFAR framework by fully exploiting the potential diversity gain, with which the detection signal-to-noise ratio (SNR) is substantially improved. Theoretical analysis suggests the achieved SNR gain of our method over traditional algorithms grows as the area size $S$ of each target on RDM, i.e. $ {\mathcal {O}}(S)$ . As demonstrated by numerical simulations and real experiments, our method dramatically improves the detection probability of both single-input and single-output (SISO) and multiple-input multiple-output (MIMO) radar systems, also greatly enriching the output point-cloud information for other sophisticated inference tasks. Our method has great potentials in the emerging automotive mmWave radars for highly accurate targets detection.