Radar specific emitter identification via the Attention-GRU model

雷达 计算机科学 算法 鉴定(生物学) 调制(音乐) 人工智能 模式识别(心理学) 电信 植物 生物 美学 哲学
作者
Wenxu Zhang,F. S. Zhang,Zhongkai Zhao,Feiran Liu
出处
期刊:Digital Signal Processing [Elsevier BV]
卷期号:142: 104198-104198
标识
DOI:10.1016/j.dsp.2023.104198
摘要

In radar specific emitter identification (SEI), various types of unintentional modulation on pulse (UMOP) are selected as the features for discriminating between different radars. Unintentional Phase Modulation on Pulse (UPMOP), a typical type of UMOP, can provide crucial information for identifying radars. In most radar SEI algorithms, sacrificing time efficiency for higher accuracy is a common trade-off. This paper proposes a method to solve this problem by combining denoised UPMOP sequences with an Attention-based Gated Recurrent Units (Attention-GRU) model, which showed an excellent performance. Firstly, the cause of UPMOP is analyzed and the phase observation model of radar emitter signals and mathematical model of UPMOP are given. Then, the least-squares method is used to eliminate the linear trend of the phase observation model and obtain a noised estimation of the UPMOP sequences. Thirdly, the uniform B-spline (UBS) curves are then used to fit the noised estimation, resulting in a denoised and refined UPMOP sequence. Finally, the Attention-GRU model is employed to extract features from the denoised UPMOP sequences to identify radar emitters automatically. Results from simulation and measured data experiments show that the overall recognition rate of the algorithm reaches over 93% and the algorithm has excellent performance, with high identification accuracy and relatively low time consumption, even in low signal-to-noise ratio (SNR) conditions.
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