Space target identification is key to missile defense. Micromotion, as an inherent attribute of the target, can be used as the theoretical basis for target recognition. Meanwhile, time-varying micro-Doppler (m-D) frequency shifts induce frequency modulations on the target echo, which can be referred to as the m-D effect. m-D features are widely used in space target recognition as it can reflect the physical attributes of the space targets. However, the traditional recognition method requires human participation, which often leads to misjudgment. In this paper, an intelligent recognition method for space target micromotion is proposed. First, accurate and suitable models of warhead and decoy are derived, and then the m-D formulae are offered. Moreover, we present a deep-learning (DL) model composed of a one-dimensional parallel structure and long short-term memory (LSTM). Then, we utilize this DL model to recognize time-frequency distribution (TFD) of different targets. Finally, simulations are performed to validate the effectiveness of the proposed method.

中文翻译：

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一维并行网络的基于微多普勒的空间目标识别

太空目标识别是导弹防御的关键。微运动作为目标的固有属性，可以用作目标识别的理论基础。同时，随时间变化的微多普勒（mD）频移在目标回波上引起频率调制，这可以称为mD效应。mD特征被广泛用于空间目标识别，因为它可以反映空间目标的物理属性。但是，传统的识别方法需要人的参与，这常常会导致错误判断。提出了一种空间目标微动的智能识别方法。首先，推导了准确，合适的弹头和诱饵模型，然后提供了mD公式。此外，我们提出了由一维并行结构和长短期记忆（LSTM）组成的深度学习（DL）模型。然后，我们利用该DL模型识别不同目标的时频分布（TFD）。最后，进行仿真以验证所提出方法的有效性。