Motion Estimation and Compensation in Automotive MIMO SAR

TL;DR

This paper presents a comprehensive method for estimating and compensating trajectory errors in automotive MIMO SAR systems, enhancing image resolution accuracy for autonomous vehicle perception.

Contribution

It introduces a complete residual motion estimation and compensation workflow tailored for automotive SAR, including analytical modeling and practical implementation guidelines.

Findings

Effective error compensation demonstrated with experimental data

Improved SAR image resolution in automotive scenarios

Analytical models of motion errors validated experimentally

Abstract

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.