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@article{158013,
        author = {M. Khumalo and M. Muduva and E.Tarambiwa and V. Musanga and R. Chiwariro},
        title = {AIRCRAFT IDENTIFICATION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {8},
        pages = {713-724},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158013},
        abstract = {Positive aircraft identification plays a crucial role in ensuring the security of the airspace, the safety of the populace, state resources and military establishments. Aircraft identification aids in air traffic management by positively identifying each aircraft entering monitored airspace. Automatic target recognition has allowed the utilization of machine learning algorithms for the classification of aircraft types. Machine learning as a sub-field of artificial intelligence is disrupting many fields by facilitating computers to learn the rom data they are exposed to on their own. This study dives into machine learning algorithms to try and pick one that can be best used for classifying aircraft as friend or foe. In this study, the researchers focused on supervised machine learning for the classification task. Various classification algorithms were implemented in this study to train models and evaluate their accuracy. The algorithms were trained using a dataset made up of motion features extracted from aircraft flight track data. The study showed that the hat classification of aircraft can be achieved by training the models using the aircraft motion features.},
        keywords = {Aircraft identification, machine learning, supervised learning},
        month = {},
        }