Advances in signal processing technology for electronic warfare

Stephens J.P.

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Advances in signal processing technology for electronic warfare

机译：电子战信号处理技术的进步

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The denial of effective communications by enemy forces during hostile military operations has been a primary concern for military commanders since the inception of radio communications on the battlefield before World War II. Since then, the electromagnetic environment has been in a constant state of evolution toward more sophisticated jam-resistant and convert forms of modulation. For example, exotic modulation techniques employing spread spectrum (SS) signaling are routinely used by our adversaries to provide their communication links an advantage over US and Allied jammers. These same spread spectrum modulation techniques are being refined to provide convert, low probability-of-intercept (LPI) features to the unintended interceptor. The thrust of this paper focuses on developments in the theory and algorithms for detection, characterization, and exploitation of advanced waveforms using new mathematical signal processing tools. Specifically, quadratic time-frequency signal representations, wavelet transforms, and cyclostationary signal processing are introduced. This overview demonstrates the importance of these advanced techniques in a clear and concise manner. Applications and future research activities are described.

机译：自第二次世界大战前在战场上开始无线电通讯以来，敌方在敌对军事行动中拒绝有效通讯一直是军事指挥官的主要关切。从那时起，电磁环境一直处在向更复杂的抗干扰和转换调制形式发展的恒定状态。例如，我们的对手通常会使用采用扩频（SS）信号的奇异调制技术，以提供其通信链路优于美国和盟军干扰源的优势。对这些相同的扩频调制技术进行了改进，以向意想不到的拦截器提供转换的低拦截概率（LPI）功能。本文的重点是使用新的数学信号处理工具检测，表征和利用高级波形的理论和算法的发展。具体来说，介绍了二次时频信号表示，小波变换和循环平稳信号处理。本概述以简洁明了的方式展示了这些先进技术的重要性。描述了应用程序和未来的研究活动。

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《IEEE Aerospace and Electronic Systems Magazine》 |1996年第11期|P.31-38|共8页
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Stephens J.P.;
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1. Advances in signal processing technology for electronic warfare [J] . Stephens J.P. IEEE Aerospace and Electronic Systems Magazine . 1996,第11期

机译：电子战信号处理技术的进步
2. Signal-Generation Advances Support Electronic Warfare's Evolution [J] . JEAN-JACQUES DELISLE Microwaves & RF . 2014,第12期

机译：信号生成的进步支持电子战的发展
3. Discrete Electronic Warfare Signal Processing using Compressed Sensing Based on Random Modulator Pre-integrator [J] . Rao M. Sreenivasa, Mishra Chandan C., Naik K. Krishna, Defence Science Journal . 2015,第6期

机译：基于随机调制器预积分器的压缩感知离散电子战信号处理
4. Advances in signal processing technology for electronic warfare [C] . Stephens, J.P. . 1996

机译：电子战信号处理技术的进步
5. Technology in warfare: The electronic dimension, the role of electronic warfare since its inception into a central aspect of the Gulf War in 1991. [D] . Baram, Abdul Karin. 2003

机译：战争中的技术：电子领域，自1991年电子海湾战争爆发以来，电子战争的作用。
6. New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle [O] . Claire Meagher, Enrico Franco, Ruth Turk, 2020

机译：机能学传感器技术和信号处理的新进展：肌肉记录的有效性和评估者内部可靠性
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Advances in signal processing technology for electronic warfare

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