An Integral Automotive SAR Imaging Algorithm via Omega-K and Contrast-Based WPGA

Automotive synthetic aperture radar (Auto-SAR) imaging can provide high-resolution images for vehicular environment perception and localization. Due to the severe motion errors associated with jerks and bumps of the vehicle, autofocusing plays a crucial role in Auto-SAR imaging. This paper aims to provide an integral Auto-SAR imaging workflow via the Omega-K algorithm and a contrast-based WPGA (CB-WPGA) method. Based on the analysis of phase error characteristics after sub-aperture (SA) imaging, CB-WPGA, which selects range cells and weights the PGA kernel based on the contrast, is proposed to enhance the robustness of PGA in automotive scenarios. Finally, the superiority of the proposed technique is showcased by employing experimental data obtained from a 77-GHz radar installed on a vehicle.