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Open AccessArticle

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

by Chun Yang 1,‡, Arash Mohammadi 2,*,†,‡ and Qing-Wei Chen 1
1
College of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
2
Concordia Institute for Information System Engineering, Concordia University, Montreal, QC H3G-1M8, Canada
*
Author to whom correspondence should be addressed.
Current address: 1455 De Maisonneuve Blvd. W. EV-009.187, Montreal, QC HG-1M8, Canada
These authors contributed equally to this work.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Sensors 2016, 16(11), 1835; https://doi.org/10.3390/s16111835
Received: 26 July 2016 / Revised: 23 October 2016 / Accepted: 25 October 2016 / Published: 2 November 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts. View Full-Text
Keywords: fault detection; fault isolation; multi-sensor systems; information fusion; integrated navigation system; interactive multiple models fault detection; fault isolation; multi-sensor systems; information fusion; integrated navigation system; interactive multiple models
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Yang, C.; Mohammadi, A.; Chen, Q.-W. Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter. Sensors 2016, 16, 1835.

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