Graph-based sequential beamforming

Author:

Park Yongsung1ORCID,Meyer Florian1ORCID,Gerstoft Peter1ORCID

Affiliation:

1. Scripps Institution of Oceanography, University of California San Diego , La Jolla, California 92093-0238, USA

Abstract

This paper presents a Bayesian estimation method for sequential direction finding. The proposed method estimates the number of directions of arrivals (DOAs) and their DOAs performing operations on the factor graph. The graph represents a statistical model for sequential beamforming. At each time step, belief propagation predicts the number of DOAs and their DOAs using posterior probability density functions (pdfs) from the previous time and a different Bernoulli-von Mises state transition model. Variational Bayesian inference then updates the number of DOAs and their DOAs. The method promotes sparse solutions through a Bernoulli-Gaussian amplitude model, is gridless, and provides marginal posterior pdfs from which DOA estimates and their uncertainties can be extracted. Compared to nonsequential approaches, the method can reduce DOA estimation errors in scenarios involving multiple time steps and time-varying DOAs. Simulation results demonstrate performance improvements compared to state-of-the-art methods. The proposed method is evaluated using ocean acoustic experimental data.

Funder

Office of Naval Research

Publisher

Acoustical Society of America (ASA)

Subject

Acoustics and Ultrasonics,Arts and Humanities (miscellaneous)

Reference75 articles.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sparse Bayesian learning based on spatio-temporal structure-aware for matched field processing;The Journal of the Acoustical Society of America;2024-01-01

2. On the limits of distinguishing seabed types via ambient acoustic sound;The Journal of the Acoustical Society of America;2023-11-01

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