Sparse Bayesian learning for wideband direction-of-arrival estimation via beamformer power outputs in a strong interference environment

Yahao Zhang; Yixin Yang; Long Yang; Yong Wang

doi:10.1121/10.0009360

Sparse Bayesian learning for wideband direction-of-arrival estimation via beamformer power outputs in a strong interference environment

JASA Express Lett. 2022 Jan;2(1):014801. doi: 10.1121/10.0009360.

Authors

Yahao Zhang¹, Yixin Yang¹, Long Yang¹, Yong Wang¹

Affiliation

¹ School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China yahaozhang@mail.nwpu.edu.cn, yxyang@nwpu.edu.cn, lyang@nwpu.edu.cn, yongwang@nwpu.edu.cn.

PMID: 36154224
DOI: 10.1121/10.0009360

Abstract

Sparse Bayesian learning (SBL) offers a useful tool for wideband direction-of-arrival (DOA) estimation, but its performance is limited in the presence of strong interferences. To solve this problem, this letter attempts to extend the SBL to estimate DOAs via the beamformer power outputs (BPO) because the beamformer can efficiently suppress the interferences. A Bayesian probabilistic model effective for the BPO is proposed. Based on this, a BPO-based SBL method is put forward by adopting the variational Bayesian inference to estimate the DOAs from the BPO. Simulation and experimental results confirm the good performance of the proposed method.