Classification of ground targets based on radar micro-doppler signatures using deep learning and conventional supervised learning methods

Abstract

Radar has great potential in military and civilian areas, including automobile anti-collision, battlefield surveillance, etc., due to its high penetration and allweather capability. On the basis of traditional targets detection, targets classification can be realized. In this paper, a comparison of targets classification between deep learning (Deep Convolutional Neural Networks (DCNNs)) and conventional supervised learning methods (Support Vector Machine (SVM), Naive Bayes (NB) and SVM-Bayes fusion algorithm) has been made. Furthermore, several factors affecting the accuracy of classifying targets including SNR, decrease of samples, have been researched and discussed. We employ a K-band Doppler radar to acquire the raw signal due to its stationary clutter-rejection, movement detection ability and short wavelength. Then Shorttime Fourier Transform (STFT) is applied to the raw signal to characterize micro-Doppler signatures which is the fundament of the classification process. We adopt the DCNNs to deal with the spectrograms directly, while features have been designed and extracted for classification with conventional supervised learning methods. It is shown that the DCNN can achieve average accuracy approximately 99.4% followed by SVM-Bayes fusion algorithm reaching around 95.8%, while the accuracy for SVM and NB is about 94.4% and 91% respectively.