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Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks

Bandyopadhyay, Saptarshi and Chung, Soon-Jo (2018) Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks. Automatica, 97 . pp. 7-17. ISSN 0005-1098. 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:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.automatica.2018.07.013DOIArticle
http://arxiv.org/abs/1712.04062arXivDiscussion Paper
ORCID:
AuthorORCID
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2018 Elsevier Ltd. Received 14 January 2016, Revised 15 May 2018, Accepted 29 June 2018, Available online 9 August 2018. S. Bandyopadhyay and S.-J. Chung were supported in part by the AFOSR grant (FA95501210193) and the National Science Foundation , USA grant (1253758 & 1664186). The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Tamas Keviczky under the direction of Editor Christos G. Cassandras.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA95501210193
NSFIIS-1253758
NSFIIS-1664186
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
Official Citation:Saptarshi Bandyopadhyay, Soon-Jo Chung, Distributed Bayesian filtering using logarithmic opinion pool for dynamic sensor networks, Automatica, Volume 97, 2018, Pages 7-17, ISSN 0005-1098, https://doi.org/10.1016/j.automatica.2018.07.013. (http://www.sciencedirect.com/science/article/pii/S0005109818303704)
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:13 Aug 2018 22:32

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