Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat Maneuvering
Air surveillance radar tracking systems present a variety of known problems related to uncertainty and lack of accurately in radar measurements used as source in these systems. In this work, we feature the theoretical aspects of a tracking algorithm based on neural network paradigm where, from discr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/160718 |
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| _version_ | 1849399701191786496 |
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| author | Antonio Navidad Pineda Luis Usero Aragonés José Raúl Fernández del Castillo Díez Miguel Ángel Patricio Guisado |
| author_facet | Antonio Navidad Pineda Luis Usero Aragonés José Raúl Fernández del Castillo Díez Miguel Ángel Patricio Guisado |
| author_sort | Antonio Navidad Pineda |
| collection | DOAJ |
| description | Air surveillance radar tracking systems present a variety of known problems related to uncertainty and lack of accurately in radar measurements used as source in these systems. In this work, we feature the theoretical aspects of a tracking algorithm based on neural network paradigm where, from discrete measurements provided by surveillance radar, the objective will be to estimate the target state for tracking purposes as accuracy as possible. The absence of an optimal statistical solution makes the featured neural network attractive despite the availability of complex and well-known filtering algorithms. Neural networks exhibit universal mapping capabilities that allow them to be used as a control tool for capturing hidden information about models learned from a dataset. We use these capabilities to let the network learn, not only from the received radar measurement information, but also from the aircraft maneuvering context, contextual information, where tracking application is working, taking into account this new contextual information which could be obtained from predefined, commonly used, and well-known aircraft trajectories. In this case study, the proposed solution is applied to a typical air combat maneuvering, a dogfight, a form of aerial combat between fighter aircraft. Advantages of integrating contextual information in a neural network tracking approach are demonstrated. |
| format | Article |
| id | doaj-art-9c689bc953754d9ab344f2e66f9e9350 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-9c689bc953754d9ab344f2e66f9e93502025-08-20T03:38:16ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-08-01910.1155/2013/160718Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat ManeuveringAntonio Navidad PinedaLuis Usero AragonésJosé Raúl Fernández del Castillo DíezMiguel Ángel Patricio GuisadoAir surveillance radar tracking systems present a variety of known problems related to uncertainty and lack of accurately in radar measurements used as source in these systems. In this work, we feature the theoretical aspects of a tracking algorithm based on neural network paradigm where, from discrete measurements provided by surveillance radar, the objective will be to estimate the target state for tracking purposes as accuracy as possible. The absence of an optimal statistical solution makes the featured neural network attractive despite the availability of complex and well-known filtering algorithms. Neural networks exhibit universal mapping capabilities that allow them to be used as a control tool for capturing hidden information about models learned from a dataset. We use these capabilities to let the network learn, not only from the received radar measurement information, but also from the aircraft maneuvering context, contextual information, where tracking application is working, taking into account this new contextual information which could be obtained from predefined, commonly used, and well-known aircraft trajectories. In this case study, the proposed solution is applied to a typical air combat maneuvering, a dogfight, a form of aerial combat between fighter aircraft. Advantages of integrating contextual information in a neural network tracking approach are demonstrated.https://doi.org/10.1155/2013/160718 |
| spellingShingle | Antonio Navidad Pineda Luis Usero Aragonés José Raúl Fernández del Castillo Díez Miguel Ángel Patricio Guisado Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat Maneuvering International Journal of Distributed Sensor Networks |
| title | Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat Maneuvering |
| title_full | Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat Maneuvering |
| title_fullStr | Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat Maneuvering |
| title_full_unstemmed | Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat Maneuvering |
| title_short | Radar Tracking System Using Contextual Information on a Neural Network Architecture in Air Combat Maneuvering |
| title_sort | radar tracking system using contextual information on a neural network architecture in air combat maneuvering |
| url | https://doi.org/10.1155/2013/160718 |
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