A distributed Kalman filter with global covariance

J. Sijs, M. Lazar

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

9 Citations (Scopus)


Most distributed Kalman filtering (DKF) algorithms for sensor networks calculate a local estimate of the global state-vector in each node. An important challenge within distributed estimation is that all sensors in the network contribute to the local estimate in each node. In this paper, a novel DKF algorithm is proposed with the goal of attaining the above property, which is denoted as global covariance. In the considered DKF set-up each node performs two steps iteratively, i.e., it runs a standard Kalman Alter using local measurements and then fuses the resulting estimates with the ones received from its neighboring nodes. The distinguishing aspect of this set-up is a novel state-fusion method, i.e., ellipsoidal intersection (EI). The main contribution consists of a proof that the proposed DKF algorithm, in combination with EI for state-fusion, enjoys the desired property under similar conditions that should hold for observability of standard Kalman filters. The advantages of developed DKF with respect to alternative DKF algorithms are illustrated for a benchmark example of cooperative adaptive cruise control
Original languageEnglish
Title of host publicationProceedings of the 30th American Control Conference (ACC), 29 June - 1 July 2011, San Francisco, California
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4577-0080-4
Publication statusPublished - 2011


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