Total Variation Compressive Sensing for Extended Targets in MIMO Radar

Conference paper (2022)

Authors

I. Roldan Montero

F. Fioranelli

Alexander Yarovoy

DOI: https://doi.org/10.1109/SAM53842.2022.9827822

Compressive Sensing Extended Targets MIMO automotive radar Total Variation Normalization

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Published Date

2022

Language

English

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Abstract

The problem of extended target cross-section estimation has been considered. A two-step method based on the Total Variation Compressive Sensing theory has been proposed to solve it. First, a coarse estimation of the target cross-section is performed with classical beamforming methods, and then Compressive Sensing algorithms have been applied to refine it. To the best of the authors' knowledge, this is the first time this approach has been applied to automotive radar signals. The method has been verified simulating extended targets as scatter point clouds and computing the response in a uniform rectangular array. Two metrics have been used, the Intersection over Union and a pseudo Integrated Sidelobe Level. Significant improvements in both metrics compared with classical beamforming methods have been demonstrated.

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