Park Yongsung, Meyer Florian, Gerstoft Peter
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238, USA.
J Acoust Soc Am. 2023 Jan;153(1):723. doi: 10.1121/10.0016876.
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.
本文提出了一种用于顺序测向的贝叶斯估计方法。所提出的方法通过在因子图上进行操作来估计到达方向（DOA）的数量及其DOA。该图表示顺序波束形成的统计模型。在每个时间步，信念传播使用来自上一个时间的后验概率密度函数（pdf）和不同的伯努利 - 冯·米塞斯状态转移模型来预测DOA的数量及其DOA。然后，变分贝叶斯推理更新DOA的数量及其DOA。该方法通过伯努利 - 高斯幅度模型促进稀疏解，无需网格，并提供边际后验pdf，从中可以提取DOA估计及其不确定性。与非顺序方法相比，该方法可以减少在涉及多个时间步和时变DOA的场景中的DOA估计误差。仿真结果表明与现有方法相比性能有所提高。所提出的方法使用海洋声学实验数据进行评估。
