Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 892-897 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9798350323047 |
| ISBN (Print) | 979-8-3503-2305-4 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023 - Genoa, Italien Dauer: 15 Nov. 2023 → 17 Nov. 2023 |
Publikationsreihe
| Name | IEEE Conference on Antenna Measurements and Applications, CAMA |
|---|---|
| ISSN (Print) | 2474-1760 |
| ISSN (elektronisch) | 2643-6795 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Physik und Astronomie (insg.)
- Instrumentierung
- Physik und Astronomie (insg.)
- Strahlung
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- BibTex
- RIS
2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. S. 892-897 (IEEE Conference on Antenna Measurements and Applications, CAMA).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - DoA Estimation in Automotive MIMO Radar With Sparse Array via Fast Variational Bayesian Method
AU - Jauch, Alisa
AU - Meinl, Frank
AU - Blume, Holger
N1 - 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.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Bayes methods
KW - direction-of-arrival estimation
KW - MIMO radar
KW - sparse array
KW - sparse recovery
KW - super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85182284031&partnerID=8YFLogxK
U2 - 10.1109/CAMA57522.2023.10352889
DO - 10.1109/CAMA57522.2023.10352889
M3 - Conference contribution
AN - SCOPUS:85182284031
SN - 979-8-3503-2305-4
T3 - IEEE Conference on Antenna Measurements and Applications, CAMA
SP - 892
EP - 897
BT - 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023
Y2 - 15 November 2023 through 17 November 2023
ER -