Class Identification of Aircrafts by Means of Artificial Neural Networks Trained with Simulated Radar Signatures
| Autor: | Jurado Lucena, A., Montiel, I., Escot Bocanegra, D., Poyatos Martínez, D. |
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| Přispěvatelé: | Instituto Nacional de Técnica Aeroespacial (INTA) |
| Rok vydání: | 2011 |
| Zdroj: | DIGITAL.INTA Repositorio Digital del Instituto Nacional de Técnica Aeroespacial instname |
| Popis: | Non-Cooperative Target Recognition (NCTR) of aircrafts from radar measurements is a formidable problem that has drawn the attention of engineers and scientists over the last years. NCTR techniques typically involve a database with a huge amount of information from different known targets and a reliable identification algorithm able to highlight the likeness between measured and stored data. This paper uses High Resolution Range Profiles produced with a high-frequency software tool to train Arti cial Neural Networks for distinguishing between different classes of aircrafts. Actual data from the ORFEO measurement campaign are used to assess the performance of the trained networks. The authors would like to thank the members of NATO-RTO SET112 Task Group on “Advanced analysis and Recognition of Radar Signatures for Non-Cooperative Target Identification”, for their helpful discussions and for the availability of actual data obtained through the measurements campaigns organized in the framework of this group. The work presented in this paper has been supported by INTA under the Electronic Warfare and Non-Cooperative Target Identification project. Peerreview |
| Databáze: | OpenAIRE |
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