Graph-based sequential beamforming

TL;DR

This paper introduces a Bayesian sequential beamforming method that estimates multiple directions of arrival with uncertainty quantification, improving accuracy over nonsequential methods in dynamic scenarios.

Contribution

It develops a gridless Bayesian approach using belief propagation and variational inference for sequential DOA estimation with uncertainty quantification.

Findings

Reduces DOA estimation errors in multi-step, time-varying scenarios

Provides marginal posterior PDFs for DOA estimates and uncertainties

Demonstrates improved performance on simulated and ocean acoustic data

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.