Abstract
The purpose of this paper is to present a multi-sensorial detection method for discovering and obtaining characteristics of flying Unmanned Aerial Vehicles (UAVs) in restricted areas. Different solutions may be applied for this purpose: radio signals analysis, acoustic patterns analysis, video processing, IR imaging, RADAR, LIDAR etc. The new Concurrent Neural Networks (CNN) classification has been introduced as a collection of low-volume neural networks that perform parallel classification. In the present paper the identification and classification of drones is analyzed employing two CNNs, a multilayer perceptron (MLP) for acoustic pattern recognition and a self – organizing map (SOM) to recognize an object from a video stream.
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Notes
- 1.
SAR – Search and Rescue.
- 2.
EO/IR – Electro-Optic/Infrared camera sensing.
- 3.
FMCW – Frequency Modulated Continuous Wave.
- 4.
UAS – Unmanned Aerial Systems.
- 5.
EO – Electro-Optical.
- 6.
MFCC – Mel Frequency Cepstral Coefficients - a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency.
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Acknowledgement
This work was supported by a grant of the Ministry of Innovation and Research, UEFISCDI, project number 9SOL/12.04.2018 within PNCDI III.
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Dumitrescu, C., Minea, M., Ciotirnae, P. (2020). UAV Detection Employing Sensor Data Fusion and Artificial Intelligence. In: Borzemski, L., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1050. Springer, Cham. https://doi.org/10.1007/978-3-030-30440-9_13
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