A Novel Approach for Recognition and Identification of Low-Level Flight Military Aircraft using Naive Bayes Classifier and Information Fusion

(1) * Arwin Datumaya Wahyudi Sumari Mail (Cognitive Artificial Intelligence Research Group (CAIRG), Department of Electrical Engineering, Politeknik Negeri Malang, and Abdulrachman Saleh Air Force Base, 2nd Operation Command, Indonesian Air Force, Indonesia)
(2) Afifah Millatina Nugraheni Mail (Department of Information Technology, Politeknik Negeri Malang, Indonesia)
(3) Yoppy Yunhasnawa Mail (Department of Information Technology, Politeknik Negeri Malang, Indonesia)
*corresponding author

Abstract


A problem that has been faced by the Radar is if the aircraft flies at low level or near to the surface so its coming in the aerial-surveillance airspace cannot be detected and endangers the air sovereignty. The aircraft can be recognized and identified by carrying out a technique called Visual Aircraft Recognition (VACR) using a binocular. This technique requires military personnel that has capability carrying out the air surveillance from the ground. Surveillance is a time-consuming and tiring task so it can cause fatigue and impact to the results of the recognition and identification. To cope with this problem, we have designed and implemented a novel recognition and identification method using the combination of Naive Bayes Classifier (NBC) and information fusion. By using a dataset that consists of 45 military aircrafts, 35 civilian aircrafts, 40 military helicopters, and 35 civilian helicopters with 80:20 dataset distribution for the training scheme and the validation one, we obtained the recognition accuracy of 87.1%. We also found that the recognition and identification process can be speeded up 1.2 seconds when using information fusion.

Keywords


Artificial Intelligence, Information Fusion, Low-Level Flight, Military Aircraft, Naive Bayes Classifier, Recognition and Identification

   

DOI

https://doi.org/10.29099/ijair.v6i1.248
      

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10.29099/ijair.v6i1.248 Abstract views : 275

   

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