Distributed Kalman Filtering: Consensus, Diffusion, and Mixed

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)

Abstract

A distributed Kalman filtering technique is developed for tracking state-space processes via sensor networks. Considering the optimal solution to multi-agent sequential filtering of linear Gaussian state-space processes, that is the centralized Kalman filter, this work focuses on decomposing and distributing the operation of the centralized Kalman filter among the agents of the sensor network. This decomposition is performed in a fashion that allows each agent to maintain a local Kalman filtering operation and an intermediate estimate of the state vector, providing for a robust distributed Kalman filtering technique that is scalable with the size of the network. In contrast to state-of-the-art distributed Kalman filtering approaches that focus on the use of consensus or diffusion as the basis of their information fusion, a mixed approach is proposed that exploits advantages of both methods. The performance of the proposed distributed Kalman filtering technique is verified in a simulation example, where the proposed technique is shown to outperform state-of-the-art distributed Kalman filtering algorithms.

Original languageEnglish
Title of host publication2018 IEEE Conference on Control Technology and Applications, CCTA 2018
PublisherIEEE
Pages1126-1132
Number of pages7
ISBN (Electronic)9781538676981
DOIs
Publication statusPublished - 26 Oct 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Conference on Control Technology and Applications - Copenhagen, Denmark
Duration: 21 Aug 201824 Aug 2018
Conference number: 2

Conference

ConferenceIEEE Conference on Control Technology and Applications
Abbreviated titleCCTA
CountryDenmark
CityCopenhagen
Period21/08/201824/08/2018

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