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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
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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; https://doi.org/10.3390/s16091407
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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