Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks
- Creators
- Bandyopadhyay, Saptarshi
-
Chung, Soon-Jo
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.
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.Attached Files
Submitted - 1712.04062.pdf
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Additional details
- Eprint ID
- 87603
- Resolver ID
- CaltechAUTHORS:20180706-132824806
- Air Force Office of Scientific Research (AFOSR)
- FA95501210193
- NSF
- IIS-1253758
- NSF
- IIS-1664186
- Created
-
2018-07-06Created from EPrint's datestamp field
- Updated
-
2021-11-15Created from EPrint's last_modified field
- Caltech groups
- GALCIT