A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems
Ammar Mohanna,
Christian Gianoglio,
Ali Rizik,
Maurizio Valle
Affiliations
Ammar Mohanna
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11, 16145 Genoa, Italy
Christian Gianoglio
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11, 16145 Genoa, Italy
Ali Rizik
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11, 16145 Genoa, Italy
Maurizio Valle
Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia 11, 16145 Genoa, Italy
The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%.
