Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm.
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.
State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China. hopp.kingson@gmail.com.
Baoliang
B
Sun
State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China. jiangchunwh@bit.edu.cn.
Chunlan
C
Jiang
State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China. iseagle@bit.edu.cn.
Ming
M
Li
fuzzy neural network; interacting multiple model; multi-sensing data fusion; target tracking; wireless sensor network