In this paper, we investigate effect of different Time-Frequency (TF) analysis of micro-Doppler signature for robust and efficient automatic target classification in next-generation radar systems. Indeed, we provide a novel mathematical baseband model for the non-stationary reflected signal of the targets and explore how Short Time Fourier Transform (STFT) and General Linear Chirplet Transform (GLCT) are useful in categorization of different ground moving targets, including walking person, animal, light vehicles and drone. To simulate the micro-Doppler signature, we will introduce a simple, but sufficiently realistic signal model. Simulation and results which are provided for various scenarios, illustrate the importance of appropriate TF representation in target classification.

在本文中，我们研究了微多普勒信号的不同时频（TF）分析对下一代雷达系统中强大而有效的自动目标分类的影响。实际上，我们为目标的非平稳反射信号提供了一个新颖的数学基带模型，并探讨了短时傅立叶变换（STFT）和通用线性Chirplet变换（GLCT）在分类不同的地面移动目标（包括步行者）时如何有用，动物，轻型车辆和无人机。为了模拟微多普勒信号，我们将介绍一个简单但足够现实的信号模型。针对各种情况提供的仿真和结果说明了目标分类中适当TF表示的重要性。