DoA Estimation in Automotive MIMO Radar With Sparse Array via Fast Variational Bayesian Method

authored by

Alisa Jauch, Frank Meinl, Holger Blume

Abstract

A lot of effort has been made recently to increase the angular performance of automotive radar sensors. At the same time, low system costs are still favored so that technical solutions like multiple-input and multiple-output (MIMO) and sparse arrays have found their way into the market successfully. With these techniques, however, some tradeoffs regarding sidelobe level and ambiguity are inevitable which impose new challenges to angle estimation methods. This paper presents a novel Fast Variational Bayesian (FVB) based direction of arrival (DoA) estimator suitable for mitigating the effects of high sidelobes in sparse arrays. The proposed algorithm is firstly adapted to automotive MIMO radar. Super-resolution and multi-target capability are validated by extensive experimental evaluations based on synthetic and measured radar data. The presented approach performs best in separating closely spaced reflections amongst all other accelerated Sparse Bayesian algorithms reported in literature so far. Furthermore, it is shown that FVB can outperform other state-of-the-art algorithms like beamforming or maximum likelihood methods.

Organisation(s)

Architectures and Systems Section

External Organisation(s)

Robert Bosch GmbH

Type

Conference contribution

Pages

892-897

No. of pages

6

Publication date

2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Networks and Communications, Instrumentation, Radiation

Electronic version(s)

https://doi.org/10.1109/CAMA57522.2023.10352889 (Access: Closed)