Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
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An optimal two-stage algorithm for highly maneuvering targets tracking
Signal Processing
Video target tracking by using competitive neural networks
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Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Information Sciences: an International Journal
Transmission rate allocation in multisensor target tracking over a shared network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic energy management with improved particle filter prediction in wireless sensor networks
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
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A fast target maneuver detection and highly accurate tracking technique using a neural fuzzy network based on a Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of the Kalman filter or modeling the maneuvering target, this new algorithm, SONFIN, can always find an economic network size with a fast learning process. Simulation results show that KF-SONFIN is superior to traditional IE and VDF methods in estimation accuracy.