Abstract
Due to the bad performance of the common method for track to track fusion under complicated disturbing environment and radar netting dynamically, an algorithm for track to track fusion based on NFE model (neural network-fuzzy reasoning-expert system) is proposed. By making good use of the self-learning, self-reasoning ability and robustness of fuzzy neural network and expert system, this approach can adapt to the changes of the system environment and find the optimal algorithm. Simulation results show that this algorithm can still get results with high precision even if under various complicated conditions, such as sensors' reliabilities being low, a large amount of outliers existing in the whole measurement data set, sensors broken down or destroyed, radars netting over again, and so on.
| Original language | English |
|---|---|
| Pages | 3160-3164 |
| Number of pages | 5 |
| State | Published - 2004 |
| Event | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004 |
Conference
| Conference | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings |
|---|---|
| Country/Territory | China |
| City | Hangzhou |
| Period | 15/06/04 → 19/06/04 |
Keywords
- Expert system
- Fuzzy neural network
- NFE model
- Track to track fusion
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