Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-27 DOI:10.1109/TNSE.2025.3554729

Parth Paritosh;Nikolay Atanasov;Sonia Martínez

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Abstract

In this paper, we design and analyze distributed Bayesian estimation algorithms for sensor networks. We consider estimation problems, such as cooperative localization and federated learning, where the data collected at any agent depends on a subset of all variables of interest. We provide a unified formulation of centralized, distributed and marginal probabilistic estimation as a Bayesian density estimation problem using data from non-linear likelihoods at agent. We develop distributed estimation algorithms based on stochastic mirror descent with appropriate regularization to enforce distributed or marginal density constraints. We prove almost-sure convergence to the optimal set of probabilities at each agent in both the distributed and marginal settings. Finally, we present Gaussian density versions of these algorithms and compare them to belief propagation variants in a node localization problem with relative position measurements. We also demonstrate our algorithms in a multi-agent mapping problem using LiDAR data.

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传感器网络中的分布式贝叶斯估计：关于边际密度的共识

本文设计并分析了用于传感器网络的分布式贝叶斯估计算法。我们考虑估计问题，如合作定位和联邦学习，其中在任何代理上收集的数据依赖于所有感兴趣的变量的子集。我们提供了一个统一的公式集中，分布和边际概率估计作为贝叶斯密度估计问题，使用数据从非线性似然在代理。我们开发了基于随机镜像下降的分布式估计算法，并采用适当的正则化来强制执行分布或边际密度约束。我们证明了在分布设置和边际设置下，每个代理几乎肯定收敛到最优概率集。最后，我们提出了这些算法的高斯密度版本，并将它们与具有相对位置测量的节点定位问题中的信念传播变体进行了比较。我们还使用激光雷达数据在一个多智能体映射问题中演示了我们的算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.