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International Journal of Machine Intelligence and Sensory Signal Processing

 

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International Journal of Machine Intelligence and Sensory Signal Processing (1 paper in press)

 

Regular Issues

 

  • Self-Configuring Artificial Neural Network Applied to Unmanned Aerial Position Estimation with use of Thermal Infrared Images   Order a copy of this article
    by Wanessa Da Silva, Nandamudi Lankalapalli Vijaykumar, Haroldo Fraga De Campos Velho, Elcio Hideiti Shiguemori 
    Abstract: Applications of Unmanned Aerial Vehicles (UAVs) have had an exponential growth. Employing this technology is useful where the human intervention could be impossible, exhaustive, risky, or expensive. Many low cost applications for UAVhave motivated research for autonomous navigation. However, flying during night periods is still a challenge for UAV. For autonomous navigation, some approaches can be applied: the use of information from a Global Navigation Satellite System (GNSS), and image processing. In the case of GNSS, signal may be lost or blocked. Therefore, alternative ways other than GNSS signal deserve to be investigated for critical missions. This research proposes a method to estimate the geographic position of a UAV during the night based on thermal infrared images (TIR). An image processing procedure is applied to extract edges from satellite images under the visible band and UAV thermal infrared image. The latter process is performed by means of Artificial Neural Networks (ANNs) and correlation index. The automatic configuration of Artificial Neural Network (ANN) has been designed by optimization approach, solved by the Multiple- Particle Collision Algorithm.
    Keywords: Unmanned Aerial Vehicles; Thermal Infrared Images; Artificial Neural Network; Multiple-Particle Collision Algorithm.