Classification of UAV-to-Ground Vehicles Based on Micro-Doppler Signatures Using Singular Value Decomposition and Deep Convolutional Neural Networks

Abstract

Attack from the unmanned aerial vehicles (UAVs) has been the main means of high-precision strike. Therefore, classifying ground vehicles from the UAV with high accuracy is of great significance. In order to avoid the complex feature extracting process and realize the classification of UAV-to-ground vehicles in different situations, this paper proposed a method based on micro-Doppler signatures using singular value decomposition (SVD) and deep convolutional neural networks (DCNNs). First, models of UAV-to-ground vehicles are built to analyze the micro-Doppler components and Doppler signals in five different cases are given. Second, time-frequency spectrums of Doppler signals with low signal-to-noise ratios are improved after removing noise using SVD. Third, transfer-learning of pre-trained DCNNs is achieved using measured data and classification under various conditions is realized using the new-trained network. When there is no noise, the overall classification accuracy of two types of Doppler signals, three types of Doppler signals, four types of Doppler signals and five types of Doppler signals has reached 100%, 97%, 97%, and 96%, respectively. Comparison with current methods which need to extract micro-Doppler features by time-frequency techniques are also made. Outstanding performance proves the superiority and robustness of the proposed method.