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Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks

Bandyopadhyay, Saptarshi and Chung, Soon-Jo (2017) Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20180706-132824806

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Abstract

The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented for the problem of tracking a target dynamic model using a time-varying network of heterogeneous sensing agents. In the DBF algorithm, the sensing agents combine their normalized likelihood functions in a distributed manner using the logarithmic opinion pool and the dynamic average consensus algorithm. We show that each agent's estimated likelihood function globally exponentially converges to an error ball centered on the joint likelihood function of the centralized multi-sensor Bayesian filtering algorithm. We rigorously characterize the convergence, stability, and robustness properties of the DBF algorithm. Moreover, we provide an explicit bound on the time step size of the DBF algorithm that depends on the time-scale of the target dynamics, the desired convergence error bound, and the modeling and communication error bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into a modified form of the Kalman information filter. The performance and robust properties of the DBF algorithm are validated using numerical simulations.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1712.04062arXivDiscussion Paper
ORCID:
AuthorORCID
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2017 California Institute of Technology. S. Bandyopadhyay and S.-J. Chung were supported in part by the AFOSR grant (FA95501210193) and the NSF grant (IIS-1253758). This research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA95501210193
NSFIIS-1253758
NASA/JPL/CaltechUNSPECIFIED
Subject Keywords:Bayesian filtering, distributed estimation, sensor network, data fusion, logarithmic opinion pool
Record Number:CaltechAUTHORS:20180706-132824806
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180706-132824806
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87603
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:06 Jul 2018 21:34
Last Modified:06 Jul 2018 21:34

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