Spatially Filtered Sparse Bayesian Learning for Direction-of-Arrival Estimation with Leaky-Wave Antennas

R. Maydani; Y. Wang; J. Sarrazin; B. Ma

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arXiv · 2510.10796

Spatially Filtered Sparse Bayesian Learning for Direction-of-Arrival Estimation with Leaky-Wave Antennas

R. Maydani·Y. Wang·J. Sarrazin·B. Ma

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Abstract

Direction-of-arrival (DoA) estimation with leaky-wave antennas (LWAs) offers a compact and cost-effective alternative to conventional antenna arrays but remains challenging in the presence of coherent sources. To address this issue, we propose a spatially filtered sparse Bayesian learning (SF-SBL) framework. Firstly, the field of view (FoV) is divided into angular sectors according to the frequency beam-scanning property of LWAs, and Bayesian inverse problems are then solved within each sector to improve efficiency and reduce computational cost. Both on-grid SBL and off-grid SBL formulations are developed. Simulation results show that the proposed approach achieves robust and accurate DoA estimation, even with coherent sources.

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BibTeX RIS

R. Maydani, Y. Wang, J. Sarrazin, B. Ma. 2025-10-12. Spatially Filtered Sparse Bayesian Learning for Direction-of-Arrival Estimation with Leaky-Wave Antennas. https://arxiv.org/abs/2510.10796

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