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.
Highlights
Wireless sensor networks (WSNs) are developing toward positive intelligence, a large scale, modularization, and integration with the advancement of sensors, electronic information, and integrated technology [1,2]
An interacting multiple model (IMM) multi-node target tracking algorithm based on fuzzy neural network (FNN) is proposed to solve the maneuvering target tracking problem in WSNs
The proposed algorithm can effectively overcome the shortcoming of the IMM algorithm of requiring a priori knowledge of the system measurement errors
Summary
Wireless sensor networks (WSNs) are developing toward positive intelligence, a large scale, modularization, and integration with the advancement of sensors, electronic information, and integrated technology [1,2]. Several methods have been introduced recently to solve target tracking problems, and many scholars have introduced fuzzy technology into the IMM algorithm to estimate model parameters in real time. A fuzzy-logic-based interacting multiple model (FIMM) algorithm for tracking a maneuvering target was proposed in [16]. In [18], fuzzy logic was incorporated in a conventional IMM-PDA (probabilistic data association) method, which adopted the prediction error and change of the prediction error as fuzzy inputs The feasibility of this proposed algorithm was examined by simulation. The algorithm proposed in this study did not require the assumption of the statistical characteristics of measurement errors and exhibited good tracking performance in the network when some nodes outputted error data.
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