基于CNN IQ不平衡估计器的辐射源辨识

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摘要翻译：
特定发射器识别是将接收到的信号与唯一的发射器相关联，并通过发射器赋予每一次传输的自然发生和无意的特征（称为其射频指纹）而成为可能。本文提出了一种利用卷积神经网络来估计每个发射体的IQ不平衡参数的方法，该方法仅使用原始IQ数据作为输入。由于发射器的IQ不平衡参数不会随着调制方案的改变而改变，所以该方法能够跟踪发射器，即使它们改变了调制方案。利用仿真的正交调幅信号和相移键控信号对该方法的性能进行了评估，并考虑了信噪比、不平衡值和调制方式的影响。此外，所开发的方法被证明优于一个可比的基于特征的方法，同时做出更少的假设和使用更少的数据。
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英文标题：
《Emitter Identification Using CNN IQ Imbalance Estimators》
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作者：
Lauren J. Wong, William C. Headley, Alan J. Michaels
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency fingerprint. This work presents an approach for identifying emitters using Convolutional Neural Networks to estimate the IQ imbalance parameters of each emitter, using only raw IQ data as input. Because an emitter's IQ imbalance parameters will not change as it changes modulation schemes, the proposed approach has the ability to track emitters, even as they change modulation scheme. The performance of the developed approach is evaluated using simulated quadrature amplitude modulation and phase-shift keying signals, and the impact of signal-to-noise ratio, imbalance value, and modulation scheme are considered. Further, the developed approach is shown to outperform a comparable feature-based approach, while making fewer assumptions and using less data.
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PDF链接：
https://arxiv.org/pdf/1808.02369

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关键词：CNN 不平衡 Applications Optimization Transmission 数据 方案 emitters each CNN