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Micro-doppler-radar-based UAV detection using inception-residual neural network

Le, H. and Doan, V.-S. and Le, D.P. and Nguyen, H.-H. and Huynh-The, T. and Le-Ha, K. and Hoang, V.-P. (2020) Micro-doppler-radar-based UAV detection using inception-residual neural network. In: 13th International Conference on Advanced Technologies for Communications, ATC 2020, 8 October 2020 through 10 October 2020.

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Abstract

This paper demonstrates the performance evaluation of UAV detection based on micro-Doppler radar image data with the proposed inception-residual neural network (IRNN). Accordingly, the network is designed and analyzed by changing network hyper-parameters through experiment with the Real Doppler RAD-DAR (RDRD) dataset that is collected by the practical measurements. Numerical analysis results show that the proposed network with 16 filters yield a good trade-off between accuracy and time-consuming performances. Moreover, the network is taken into account for competing with three other networks. Due to inception-residual structure, the proposed network remarkably outperforms other ones. © 2020 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Institutes > Institute of System Integration
Identification Number: 10.1109/ATC50776.2020.9255454
Uncontrolled Keywords: Aircraft detection; Analog computers; Doppler radar; Economic and social effects; Tracking radar; Unmanned aerial vehicles (UAV); Doppler; Hyper-parameter; Micro-Doppler; Residual structure; Trade off; Neural networks
Additional Information: Conference code: 165033. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8904

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