Motion Compensation for Multiple-Input-Multiple-Output Inverse Synthetic Aperture Imaging of Automotive Targets

TL;DR

This paper evaluates three motion compensation techniques for MIMO-ISAR imaging of automotive targets, demonstrating that cross-correlation MOCOMP significantly improves image quality by 36% over other methods.

Contribution

It provides a comparative analysis of motion compensation methods specifically for MIMO-ISAR, highlighting the superior performance of cross-correlation MOCOMP in automotive radar applications.

Findings

Cross-correlation MOCOMP outperforms entropy minimization and phase gradient autofocus.

Overall improvement of 36% in image quality with the best method.

Evaluation conducted on real measurement data from Texas Instruments radar.

Abstract

Inverse synthetic aperture radar (ISAR) images generated from single-channel automotive radar data provide critical information about the shape and size of automotive targets. However, the quality of ISAR images degrades due to road clutter and when translational and higher order rotational motions of the targets are not suitably compensated. One method to enhance the signal-to-clutter-and-noise ratio (SCNR) of the systems is to leverage the advantages of the multiple-input-multiple-output (MIMO) framework available in commercial automotive radars to generate MIMO-ISAR images. While substantial research has been devoted to motion compensation of single-channel ISAR images, the effectiveness of these methods for MIMO-ISAR has not been studied extensively. This paper analyzes the performance of three popular motion compensation techniques - entropy minimization, cross-correlation, and phase gradient autofocus - on MIMO-ISAR. The algorithms are evaluated on the measurement data collected using Texas Instruments millimeter-wave MIMO radar. The results indicate that the cross-correlation MOCOMP performs better than the other two MOCOMP algorithms in the MIMO configuration, with an overall improvement of 36%.