NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS - Problems of Information Technology

NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS - Problems of Information Technology

NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS - Problems of Information Technology

NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS - Problems of Information Technology

NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS - Problems of Information Technology
NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№2, 2022

NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS

Fargana J. Abdullayeva, Rashad P. Ibrahimov

The widespread use of unmanned aerial vehicles (UAVs) in both the national and military fields has made them the focus of industrial organizations. However, the use of UAVs has seriously affected the violation of the confidentiality of personal data, posed a threat to states, national institutions, nuclear power plants, historical places. One way to reduce this threat is to detect harmful UAVs. The article develops machine learning and deep learning methods based on sound signal analysis to detect harmful UAVs. Features were extracted from the sound signals and their ensemble was created. The created new data was transmitted to the input of neural network models in the form of vectors and drones were detected. The effectiveness of the proposed approach has been tested on a database open to scientific research (pp.16-23). 

Keywords: Unmanned Aerial Vehicles (UAVs), Drone, Spectrogram, Convolutional Neural Network, Radar detection system
DOI : 10.25045/jpit.v13.i2.02
References
  • Abdullayeva, F., Valikhanli, O. (2022). Development of a method for detecting GPS spoofing attacks on unmanned aerial vehicles. Problems of Information Technology, vol. 13, no. 1, pp. 3-8 http://doi.org/10.25045/jpit.v13.i1.01
  • Abdullayeva, F. (2021). Development of a support model to ensure the cyber security of UAV components, The second Karabakh War as a new generation warfare, Proceedings of the international scientific-practical conference dedicated to the anniversary of the victory achieved in the 44 days patriotic war, pp. 129-132. (Azerbaijani)
  • Young, S., Evermann, G., Kershaw, D., Moore, G., Odell., J., Ollason, D., Povey, D., Valtchev, V., Woodland, P. (2002). The HTK Book, (for HTK Version 3.2), 355 p. https://www.danielpovey.com/files/htkbook.pdf
  • Taha B. & Shoufan A. (2019). Machine Learning-Based Drone Detection and Classification. State-of-the-Art in Research, IEEE Access., 69-82.
  • Al-Emadi S., Al-Ali A., Mohammad A., Al-Ali A. (2019). Audio Based Drone Detection and Identification using Deep Learning. Proceedings of the 15th IEEE International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco. pp. 459- 464.
  • Peacock M., Johnstone MN. Towards detection and control of civilian unmanned aerial vehicles, Proceedings of the 14th Australian Information Warfare and Security Conference; 2013 December 2-4; Perth, Western Australia. p. 9-15.
  • Yaacoub JP., Noura H., Salman O., Chehab A. (2020). Security analysis of drones systems: Attacks, limitations, and recommendations. Internet of Things, 1-39.
  • Ganti SR., Kim Y. (2016). Implementation of detection and tracking mechanism for small UAS. Proceedings of the IEEE International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA. p. 1254-1260.
  • Malicious UAVs Detection. (n.d.).
    https://www.kaggle.com/sonain/malicious-uavs-detection
  • Juhyun K., Cheonbok P., Jinwoo A., Youlim K., Junghyun P., John C.G. (2017). Real-time UAV Sound Detection and Analysis System, Proceedings of the IEEE Sensors Applications Symposium (SAS), pp. 1-5.
  • Dumitrescu C., Minea M., Costea I.M., Cosmin Chiva I., Semenescu A. (2020). Development of an Acoustic System for UAV Detection. Sensors, 20(17):4870. https://doi.org/10.3390/s20174870
  • Sara Al-Emadi, Abdulla Al-Ali, Amr Mohammad, Abdulaziz Al-Ali. (2019). Audio Based Drone Detection and Identification using Deep Learning, Proceedings of the 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp 459-464
  • US Marines Test Boeing Laser To Knock Down Drones, Enemy Artillery, https://www.ibtimes.com/us-marines-test-boeing-laser-knock-down-drones-enemy-artillery-2011610, Accessed 09.03.2022.
  • Boban SJ., Ivan P., Jovan B., Boban B., Danilo O. (2022). Single and multiple drones detection and identification using RF based deep learning algorithm. Expert Systems with Applications, Vol. 187, 115928.