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Specific Emitter Identification Via Multiple Distorted Receivers

机译：通过多个失真的接收器识别特定的发射器

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摘要

Specific emitter identification (SEI) is a technique that identifies different emitters based on specific characteristics of each individual emitter, which finds broad applications in wireless communication authentication, radio monitoring, electromagnetic environment sensing, and information warfare. In this paper, we consider the SEI problem with unknown receiver distortion, which has not been well studied in the existing work. Two cooperative SEI algorithms are proposed which are composed of two steps, i.e., the feature extraction and the cooperative identification. In the first step, the received signal at each receiver is processed by the intrinsic time-scale decomposition (ITD), and the non-Gaussian features, i.e., the kurtosis and the skewness, are extracted from the decomposed signal. In the second step, the support vector machine (SVM) and the back propagation (BP) neural network are applied to fuse the features extracted from multiple distorted receivers respectively, and then determine the unknown emitters. Simulation results show that the proposed two cooperative identifiers using multiple distorted receivers outperform that using any single receiver in terms of the performance of correct identification. The significance of this paper is that the receive diversity can be achieved by the proposed cooperative identifier by using multiple distorted receivers without receiver distortion compensation.
机译：具体的发射极识别（SEI）是一种基于每个发射器的特定特征来识别不同发射器的技术，它在无线通信认证，无线电监控，电磁环境感测和​​信息战中找到广泛的应用。在本文中，我们考虑了未知接收器失真的SEI问题，在现有的工作中没有很好地研究。提出了两个协作SEI算法，其由两个步骤组成，即特征提取和协作识别。在第一步中，通过内在的时间级分解（ITD）处理每个接收器处的接收信号，并且从分解信号中提取非高斯特征，即KurtOsis和斜率。在第二步中，支持向量机（SVM）和后传播（BP）神经网络分别熔断从多个失真接收器提取的特征，然后确定未知的发射器。仿真结果表明，在正确识别性能方面，所提出的两个合作标识符越优于使用任何单个接收器的概率。本文的重要性是通过使用多扭曲的接收器而无需接收器失真补偿，可以通过所提出的协作标识符来实现接收分集。

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