Micro-Doppler-Radar-Based UAV Detection Using Inception-Residual Neural Network

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.