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Sensors 2016, 16(11), 1823; doi:10.3390/s16111823

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
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Author to whom correspondence should be addressed.
Academic Editors: Lyudmila Mihaylova, Byung-Gyu Kim and Debi Prosad Dogra
Received: 28 June 2016 / Revised: 15 October 2016 / Accepted: 18 October 2016 / Published: 1 November 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Abstract

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. View Full-Text
Keywords: wireless sensor network; multi-sensing data fusion; interacting multiple model; fuzzy neural network; target tracking wireless sensor network; multi-sensing data fusion; interacting multiple model; fuzzy neural network; target tracking
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Sun, B.; Jiang, C.; Li, M. Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm. Sensors 2016, 16, 1823.

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