Micro-Doppler-Radar-Based UAV Detection Using Inception-Residual Neural Network
- Resource Type
- Conference
- Authors
- Le, Hai; Doan, Van-Sang; Le, Dai Phong; Nguyen, Huu-Hung; Huynh-The, Thien; Le-Ha, Khanh; Hoang, Van-Phuc
- Source
- 2020 International Conference on Advanced Technologies for Communications (ATC) Advanced Technologies for Communications (ATC), 2020 International Conference on. :177-181 Oct, 2020
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Radar
Computer architecture
Drones
Training
Radar imaging
Doppler effect
Convolution
Neural network
Micro-Doppler radar
Inception-residual neural network
UAV detection
- Language
- ISSN
- 2162-1039
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.