Abstract
Data association which obtains relationship between radar measurements and existing tracks plays one important role in radar multiple-target tracking (MTT) systems. A new approach to data association based on the competitive Hopfield neural network (CHNN) is investigated, where the matching between radar measurements and existing target tracks is used as a criterion to achieve a global consideration. Embedded within the CHNN is a competitive learning algorithm that resolves the dilemma of occasional irrational solutions in traditional Hopfield neural networks. Additionally, it is also shown that our proposed CHNN-based network is guaranteed to converge to a stable state in performing data association and the CHNN-based data association combined with an MTT system demonstrates target tracking capability. Computer simulation results indicate that this approach successfully solves the data association problems.
| Original language | English |
|---|---|
| Pages (from-to) | 1180-1188 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Aerospace and Electronic Systems |
| Volume | 43 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2007 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Electrical and Electronic Engineering
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Multiple-target tracking with competitive Hopfield neural network based data association. / Chung, Yi-Nung; Chou, Pao Hua; Yang, Maw Rong; Chen, Hsin Ta.
In: IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 3, 01.07.2007, p. 1180-1188.Research output: Contribution to journal › Article
TY - JOUR
T1 - Multiple-target tracking with competitive Hopfield neural network based data association
AU - Chung, Yi-Nung
AU - Chou, Pao Hua
AU - Yang, Maw Rong
AU - Chen, Hsin Ta
PY - 2007/7/1
Y1 - 2007/7/1
N2 - Data association which obtains relationship between radar measurements and existing tracks plays one important role in radar multiple-target tracking (MTT) systems. A new approach to data association based on the competitive Hopfield neural network (CHNN) is investigated, where the matching between radar measurements and existing target tracks is used as a criterion to achieve a global consideration. Embedded within the CHNN is a competitive learning algorithm that resolves the dilemma of occasional irrational solutions in traditional Hopfield neural networks. Additionally, it is also shown that our proposed CHNN-based network is guaranteed to converge to a stable state in performing data association and the CHNN-based data association combined with an MTT system demonstrates target tracking capability. Computer simulation results indicate that this approach successfully solves the data association problems.
AB - Data association which obtains relationship between radar measurements and existing tracks plays one important role in radar multiple-target tracking (MTT) systems. A new approach to data association based on the competitive Hopfield neural network (CHNN) is investigated, where the matching between radar measurements and existing target tracks is used as a criterion to achieve a global consideration. Embedded within the CHNN is a competitive learning algorithm that resolves the dilemma of occasional irrational solutions in traditional Hopfield neural networks. Additionally, it is also shown that our proposed CHNN-based network is guaranteed to converge to a stable state in performing data association and the CHNN-based data association combined with an MTT system demonstrates target tracking capability. Computer simulation results indicate that this approach successfully solves the data association problems.
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UR - http://www.scopus.com/inward/citedby.url?scp=36849056589&partnerID=8YFLogxK
U2 - 10.1109/TAES.2007.4383609
DO - 10.1109/TAES.2007.4383609
M3 - Article
AN - SCOPUS:36849056589
VL - 43
SP - 1180
EP - 1188
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
SN - 0018-9251
IS - 3
ER -