Recognition and classification of rotorcraft by micro-doppler signatures using deep learning

Abstract

Detection and classification of rotorcraft targets are of great significance not only in civil fields but also in defense. However, up to now, it is still difficult for the traditional radar signal processing methods to detect and distinguish rotorcraft targets from various types of moving objects. Moreover, it is even more challenging to classify different types of helicopters. As the development of high-precision radar, classification of moving targets by micro-Doppler features has become a promising research topic in the modern signal processing field. In this paper, we propose to use the deep convolutional neural networks (DCNNs) in rotorcraft detection and helicopter classification based on Doppler radar signals. We apply DCNN directly to raw micro-Doppler spectrograms for rotorcraft detection and classification. The proposed DCNNs can learn the features automatically from the micro-Doppler signals without introducing any domain background knowledge. Simulated data are used in the experiments. The experimental results show that the proposed DCNNs achieve superior accuracy in rotorcraft detection and superior accuracy in helicopter classification, outperforming the traditional radar signal processing methods.