Title: Distributed Cooperative State Estimation for Dynamically Changing Networked Navigation
Author(s): Matthew Howard and Zhihua Qu
Published in: Proceedings of IEEE/ION PLANS 2016
April 11 - 14, 2016
Hyatt Regency Hotel
Savannah, GA
Pages: 987 - 993
Cite this article: Howard, Matthew, Qu, Zhihua, "Distributed Cooperative State Estimation for Dynamically Changing Networked Navigation," Proceedings of IEEE/ION PLANS 2016, Savannah, GA, April 2016, pp. 987-993.
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Abstract: In this paper, two new Cooperative Kalman-Bucy filters are derived using the matrix theoretic consensus based approach to cooperative control. The first is a centralized design of Cooperative Kalman-Bucy filter for which stabilizing and optimal gains are found to minimize error state covariance. Further, a distributed design of Cooperative Kalman-Bucy filter is also proposed by explicitly accounting for available information. Both designs of the proposed new Cooperative Kalman-Bucy filter can be applied to a team of heterogeneous time-varying systems within an incomplete, unidirectional communications network, and the overall estimation performance is superior to individual Kalman filters as long as better sensors are located at globally reachable nodes.