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Journal of Information Science and Engineering, Vol. 26 No. 4, pp. 1479-1490


The Dual-Kalman Filtering and Neural Solutions to Maneuvering Estimation Problems

YI-NUNG CHUNG1, DEND-JYI JUANG2, KUO-CHANG HU2, MING-LIANG LI2,3 AND KAI-CHIH CHUANG2
1Department of Electrical Engineering 
National Changhua University of Education 
Changhua, 500 Taiwan 
E-mail: ynchung@cc.ncue.edu.tw 
2Department of Electrical Engineering 
Da-Yeh University 
Changhua, 515 Taiwan 
3Department of Electronic Engineering 
Nan Kai University of Technology 
Nantou County, 542 Taiwan

    Tracking maneuvering targets in a radar system is more complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the maneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the maneuvering targets’ tracking problems. In this approach, two collaborative Kalman filters are devised which one for pursuing the track estimation and the other for estimating the status of maneuver. Based on this approach, the most approximate target’s acceleration can be detected and estimated in real time. Moreover, it is also shown that one Competitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability.

Keywords: maneuvering targets, Dual-Kalman filtering algorithm, competitive hopfield neural network, data association

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