An Improved Autofocus Algorithm With Bayesian Tracking of Residual Motion For Automotive MIMO-SAR Imaging

Automotive Synthetic Aperture Radar (SAR) is a promising technology for autonomous driving, where reliable perception of the environment is needed. Though, SAR focusing needs precise vehicle's trajectory knowledge, not compatible with automotive-grade navigation systems. Current autofocus algorithms refine navigation-based trajectory with radar data but do not exploit vehicle's dynamic in the residual motion estimation. This paper investigates the injection of a-priori knowledge into residual motion estimation to achieve improved and physically consistent SAR imaging. An autoregressive model of the residual velocities and Bayesian tracking via Kalman Filter are proposed and deeply studied upon application on real data acquired in an open road campaign. A new metric is introduced to quantitatively compare the outcomes: the variance of Hough lines angular coefficients. Experimental results confirm that the metric is informative, and the presence of memory in the residual motion estimation is effective in better estimating residual velocity and, consequently, improved SAR imaging.