High-Resolution mmWave SAR Imagery for Automotive Parking Assistance

• Published in: IEEE journal on miniaturization for air and space systems Vol. 4; no. 1; pp. 54 - 61
• Main Authors: Xu, Gang, Pei, Hao, Jiang, Mengjie, Chen, Jianlai, Wang, Hui, Zhang, Hui, Liu, Yanyang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automobiles; Automotive electronics; Automotive engineering; Automotive millimeter-wave (mmWave) radar; Automotive radar; High resolution; Image resolution; Image segmentation; Machine learning; Millimeter wave communication; Millimeter waves; parametric method of sparse Bayesian learning (SBL); Parking; Radar; Radar data; Radar imaging; Radar polarimetry; Signal processing algorithms; Synthetic aperture radar; synthetic aperture radar (SAR)
• ISSN: 2576-3164; 2576-3164
• DOI: 10.1109/JMASS.2022.3226771

Benefiting from the characteristics of low-cost, small-size, and high-resolution, the millimeter-wave (mmWave) radar has been gradually applied to automotive parking assistance. In this article, a novel algorithm of automotive synthetic aperture radar (SAR) imaging is proposed for the mapping of parking places. To deal with the motion error from the inaccurate speed of the radar platform, a parametric method of sparse Bayesian learning (SBL) is presented for well-focused and high-resolution SAR imaging. Then, a watershed-based SAR image segmentation algorithm is applied to detect the vehicles, which can indicate the locations of free parking spaces. Finally, the experimental analysis using 77-GHz automotive radar data is performed to confirm the effectiveness of the proposal.