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Open AccessArticle

A Novel Distributed State Estimation Algorithm with Consensus Strategy

by 1, 1,2,*, 1, 1 and 1
1
Research Institute of Information Fusion, Naval Aviation University, Yantai 264001, China
2
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2134; https://doi.org/10.3390/s19092134
Received: 26 April 2019 / Revised: 2 May 2019 / Accepted: 4 May 2019 / Published: 8 May 2019
(This article belongs to the Special Issue Consensus and Intelligent Negotiation in Sensors Networks)
Owing to its high-fault tolerance and scalability, the consensus-based paradigm has attracted immense popularity for distributed state estimation. If a target is neither observed by a certain node nor by its neighbors, this node is naive about the target. Some existing algorithms have considered the presence of naive nodes, but it takes sufficient consensus iterations for these algorithms to achieve a satisfactory performance. In practical applications, because of constrained energy and communication resources, only a limited number of iterations are allowed and thus the performance of these algorithms will be deteriorated. By fusing the measurements as well as the prior estimates of each node and its neighbors, a local optimal estimate is obtained based on the proposed distributed local maximum a posterior (MAP) estimator. With some approximations of the cross-covariance matrices and a consensus protocol incorporated into the estimation framework, a novel distributed hybrid information weighted consensus filter (DHIWCF) is proposed. Then, theoretical analysis on the guaranteed stability of the proposed DHIWCF is performed. Finally, the effectiveness and superiority of the proposed DHIWCF is evaluated. Simulation results indicate that the proposed DHIWCF can achieve an acceptable estimation performance even with a single consensus iteration. View Full-Text
Keywords: sensor networks; distributed state estimation; naive node; Kalman filter; maximum a posterior estimator; consensus filter sensor networks; distributed state estimation; naive node; Kalman filter; maximum a posterior estimator; consensus filter
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Liu, J.; Liu, Y.; Dong, K.; Ding, Z.; He, Y. A Novel Distributed State Estimation Algorithm with Consensus Strategy. Sensors 2019, 19, 2134.

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