%0 Conference Paper %1 387339eca38d4cc7a3aee2c8c5a1fd3b %A Jauch, Alisa %A Meinl, Frank %A Blume, Holger %B 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023 %C United States %D 2023 %I Institute of Electrical and Electronics Engineers Inc. %K Bayes MIMO array, direction-of-arrival estimation, methods, myown radar, recovery, sparse super-resolution %P 892--897 %R 10.1109/CAMA57522.2023.10352889 %T DoA Estimation in Automotive MIMO Radar With Sparse Array via Fast Variational Bayesian Method %X 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. %@ 979-8-3503-2305-4

@inproceedings{387339eca38d4cc7a3aee2c8c5a1fd3b, 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.}, added-at = {2024-02-05T16:11:44.000+0100}, address = {United States}, author = {Jauch, Alisa and Meinl, Frank and Blume, Holger}, biburl = {https://www.bibsonomy.org/bibtex/2b7ffd4964f6b268139d9e887ddd3e313/fabcho}, booktitle = {2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023}, doi = {10.1109/CAMA57522.2023.10352889}, interhash = {f6ddf1ec02ea1335fb7fb1285f66959f}, intrahash = {b7ffd4964f6b268139d9e887ddd3e313}, isbn = {979-8-3503-2305-4}, keywords = {Bayes MIMO array, direction-of-arrival estimation, methods, myown radar, recovery, sparse super-resolution}, language = {English}, note = {Funding Information: ACKNOWLEDGMENT This work was supported by the German Federal Ministry of Education and Research in frame of the ZuSE-KI-AVF project under grant number 16ME0062.; 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023 ; Conference date: 15-11-2023 Through 17-11-2023}, pages = {892--897}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, series = {IEEE Conference on Antenna Measurements and Applications, CAMA}, timestamp = {2024-03-05T15:42:36.000+0100}, title = {DoA Estimation in Automotive MIMO Radar With Sparse Array via Fast Variational Bayesian Method}, year = 2023 }