Abstract
Tracking maneuvering targets in a radar system is m ore complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the m aneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the m aneuvering targets' tracking problems. In this approach, two collaborativ e Kalman filters are devised which one for pursuing the track estimation and the other for estimating the status of m aneuver. Based on this approach, the m ost approximate target's acceleration can be detected and estimated in real tim e. Moreover, it is also shown that one Com petitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability.
| Original language | English |
|---|---|
| Pages (from-to) | 1479-1490 |
| Number of pages | 12 |
| Journal | Journal of Information Science and Engineering |
| Volume | 26 |
| Issue number | 4 |
| Publication status | Published - 2010 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
- Hardware and Architecture
- Library and Information Sciences
- Computational Theory and Mathematics
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The dual-kalman filtering and neural solutions to maneuvering estimation problems. / Chung, Y. I.Nung; Juang, Dend Jyi; Hu, Kuo Chang; Li, Ming Liang; Chuang, Kai Chih.
In: Journal of Information Science and Engineering, Vol. 26, No. 4, 01.07.2010, p. 1479-1490.Research output: Contribution to journal › Article
TY - JOUR
T1 - The dual-kalman filtering and neural solutions to maneuvering estimation problems
AU - Chung, Y. I.Nung
AU - Juang, Dend Jyi
AU - Hu, Kuo Chang
AU - Li, Ming Liang
AU - Chuang, Kai Chih
PY - 2010/7/1
Y1 - 2010/7/1
N2 - Tracking maneuvering targets in a radar system is m ore complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the m aneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the m aneuvering targets' tracking problems. In this approach, two collaborativ e Kalman filters are devised which one for pursuing the track estimation and the other for estimating the status of m aneuver. Based on this approach, the m ost approximate target's acceleration can be detected and estimated in real tim e. Moreover, it is also shown that one Com petitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability.
AB - Tracking maneuvering targets in a radar system is m ore complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the m aneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the m aneuvering targets' tracking problems. In this approach, two collaborativ e Kalman filters are devised which one for pursuing the track estimation and the other for estimating the status of m aneuver. Based on this approach, the m ost approximate target's acceleration can be detected and estimated in real tim e. Moreover, it is also shown that one Com petitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability.
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UR - http://www.scopus.com/inward/citedby.url?scp=77955530516&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:77955530516
VL - 26
SP - 1479
EP - 1490
JO - Journal of Information Science and Engineering
JF - Journal of Information Science and Engineering
SN - 1016-2364
IS - 4
ER -