Classifying micro-Doppler signatures using deep convolutional neural networks

An electromagnetic signal transmitted by a radar is reflected from a target then returns to the radar with the information of the target characteristics. Doppler information is commonly used to detect moving objects while suppressing clutter. In particular, the micro-Doppler signatures from nonrigid body motions contain diverse information regarding target movement [1-3]. Accordingly, the use of micro-Doppler signatures has a variety of defense, security, surveillance, and biomedicine applications, includ-ing airborne target classification, human detection, human activity classification, and drone detection.