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Bayesian methods for distributed estimation in cooperative networks

Grant number: 18/26191-0
Support type:Regular Research Grants
Duration: August 01, 2019 - July 31, 2021
Field of knowledge:Engineering - Electrical Engineering
Principal Investigator:Marcelo Gomes da Silva Bruno
Grantee:Marcelo Gomes da Silva Bruno
Home Institution: Divisão de Engenharia Eletrônica (IEE). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
Assoc. researchers:Claudio José Bordin Júnior ; Stiven Schwanz Dias

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)