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Open AccessArticle

Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input

Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Sensors 2016, 16(9), 1407;
Received: 12 March 2016 / Revised: 21 August 2016 / Accepted: 23 August 2016 / Published: 1 September 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
This paper addresses the problem of the joint estimation of system state and generalized sensor bias (GSB) under a common unknown input (UI) in the case of bias evolution in a heterogeneous sensor network. First, the equivalent UI-free GSB dynamic model is derived and the local optimal estimates of system state and sensor bias are obtained in each sensor node; Second, based on the state and bias estimates obtained by each node from its neighbors, the UI is estimated via the least-squares method, and then the state estimates are fused via consensus processing; Finally, the multi-sensor bias estimates are further refined based on the consensus estimate of the UI. A numerical example of distributed multi-sensor target tracking is presented to illustrate the proposed filter. View Full-Text
Keywords: bias estimation; state estimation; sensor registration; network consensus bias estimation; state estimation; sensor registration; network consensus
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Zhou, J.; Liang, Y.; Yang, F.; Xu, L.; Pan, Q. Multi-Sensor Consensus Estimation of State, Sensor Biases and Unknown Input. Sensors 2016, 16, 1407.

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