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Bayesian Methods for Distributed Estimation in Cooperative Networks

Abstract

We propose in this project new Bayesian algorithms for distributed estimation in cooperativenetworks using decentralized filters that operate without a global data fusioncenter. In the proposed architecture, each network node records and independently processeslocal measurements, but different nodes are also capable of communicating witheach other via message passing to build a global cooperative estimate over the network of ahidden state vector of interest. The goal, which is consistent with the state of the art in thearea, is to develop fully distributed and scalable algorithms which operate on partially connectednetworks and approximate the optimal centralized estimate, but, at the same time,have a low internode communication cost. To reach that goal, we investigate two diffusionmethods using respectively the Adapt-and-Combine (ATC) and Random Exchange (RndEx)techniques, which are formulated in a Bayesian perspective that is more general than thetraditional formulation in the literature and enables the implementation of those methodsusing particle filters in scenarios where arbitrary nonlinear and non-Gaussian state spacemodels are assumed. As an extension of this work, we also intend to generalize the proposedalgorithms to state vectors that are defined on nonlinear topological spaces (manifolds)such as hyperspheres, as opposed to linear Euclidean spaces. Finally, by extending the aforementionedtechniques to message passing algorithms in more general space-time graphs,we propose to consider the problem where multiple mobile agents linked by a partiallyconnected network cooperatively estimate their own position and, at the same time, alsocooperatively track another non-cooperative network node. Possible applications of practicalinterest for our work include identification of digital communication channels usingcooperative filter networks and surveillance of large buildings and critical infrastructureusing multiple intelligent unmanned aerial vehicles (UAVs). (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
OLIVEIRA, HALLYSSON; DIAS, STIVEN SCHWANZ; BRUNO, MARCELO GOMES DA SILVA. Cooperative Terrain Navigation Using Hybrid GMM/SMC Message Passing on Factor Graphs. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, v. 56, n. 5, p. 3958-3970, OCT 2020. Web of Science Citations: 0.
DE FIGUEREDO, CAIO G.; BORDIN JR, CLAUDIO J.; BRUNO, MARCELO G. S. Cooperative Parameter Estimation on the Unit Sphere Using a Network of Diffusion Particle Filters. IEEE SIGNAL PROCESSING LETTERS, v. 27, p. 715-719, 2020. Web of Science Citations: 0.
FERNANDES, GUILHERME C. G.; DIAS, STIVEN S.; MAXIMO, MARCOS R. O. A.; BRUNO, MARCELO G. S. Cooperative Localization for Multiple Soccer Agents Using Factor Graphs and Sequential Monte Carlo. IEEE ACCESS, v. 8, p. 213168-213184, 2020. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.