Time-varying direction-of-arrival estimation exploiting mamba network
Pavel, Saidur R. Haider, Mirza A. Zhang, Yimin D. Ding, Yanwu Shen, Dan Pham, Khanh D. Chen, Genshe
Pavel, Saidur R.
Haider, Mirza A.
Zhang, Yimin D.
Ding, Yanwu
Shen, Dan
Pham, Khanh D.
Chen, Genshe
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Issue Date
2025-06-19
Type
Conference paper
Genre
Keywords
Deep learning,Mamba,State-space model,Time-varying DOA estimation
Subjects (LCSH)
Citation
S. R. Pavel et al., "Time-Varying Direction-of-Arrival Estimation Exploiting Mamba Network," 2025 IEEE International Radar Conference (RADAR), Atlanta, GA, USA, 2025, pp. 1-5, doi: 10.1109/RADAR52380.2025.11032057.
Abstract
Direction-of-arrival (DOA) estimation for moving targets presents a significant challenge in array signal processing. Traditional DOA estimation and tracking methods often encounter limitations due to the infeasibility of acquiring large volumes of stationary data and performing subspace-based processing over many snapshots, and lead to high computational costs. Recently, deep learning techniques have been effectively applied in DOA estimation, owing to their reduced complexity during inference. In this paper, we propose the use of Mamba network as a state-space model-based approach to estimate and track DOAs that vary snapshot-by-snapshot. The proposed network is interpretable and hardware-efficient, making it advantageous for training and real-time inference. © 2025 IEEE.
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Publisher
Institute of Electrical and Electronics Engineers
Journal
Book Title
Series
AESS; IEEE
2025 IEEE International Radar Conference, RADAR 2025
3 May 2025 through 9 May 2025
Atlanta
209753
2025 IEEE International Radar Conference, RADAR 2025
3 May 2025 through 9 May 2025
Atlanta
209753
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10975764