Spatially Filtered Sparse Bayesian Learning for Direction-of-Arrival Estimation with Leaky-Wave Antennas
Rida Maydani, Julien Sarrazin, Biyun MA
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.
BibTeX
@inproceedings{icassp2026_spatiallyfiltere,
title = {Spatially Filtered Sparse Bayesian Learning for Direction-of-Arrival Estimation with Leaky-Wave Antennas},
author = {Rida Maydani and Julien Sarrazin and Biyun MA},
booktitle = {ICASSP 2026},
year = {2026}
}