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 |
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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 |
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Electrical and Electronic Engineering