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

Abstract

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">point</i> -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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area</i> -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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> of each target on RDM, i.e. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ {\mathcal {O}}(S)$</tex-math></inline-formula> . 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.