Micro-Doppler-Based Space Target Recognition with a One-Dimensional Parallel Network

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.