Abstract
The multiple-target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.
| Original language | English |
|---|---|
| Pages (from-to) | 339-342 |
| Number of pages | 4 |
| Journal | Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an |
| Volume | 31 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2008 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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Applying likelihood on Hopfield neural network for radar tracking. / Chung, Yi-Nung; Yang, Maw Rong; Juang, Dend Jyi; Hsu, Tsung Chun; Hsu, Shun Peng.
In: Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an, Vol. 31, No. 2, 01.01.2008, p. 339-342.Research output: Contribution to journal › Article
TY - JOUR
T1 - Applying likelihood on Hopfield neural network for radar tracking
AU - Chung, Yi-Nung
AU - Yang, Maw Rong
AU - Juang, Dend Jyi
AU - Hsu, Tsung Chun
AU - Hsu, Shun Peng
PY - 2008/1/1
Y1 - 2008/1/1
N2 - The multiple-target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.
AB - The multiple-target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.
UR - http://www.scopus.com/inward/record.url?scp=41849136522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=41849136522&partnerID=8YFLogxK
U2 - 10.1080/02533839.2008.9671388
DO - 10.1080/02533839.2008.9671388
M3 - Article
AN - SCOPUS:41849136522
VL - 31
SP - 339
EP - 342
JO - Chung-kuo Kung Ch'eng Hsueh K'an/Journal of the Chinese Institute of Engineers
JF - Chung-kuo Kung Ch'eng Hsueh K'an/Journal of the Chinese Institute of Engineers
SN - 0253-3839
IS - 2
ER -