摘要
通信频谱行为分析是电磁频谱对抗中提升通信态势感知层次,增强电磁侦察能力的关键手段。随着人工智能技术的发展,通信频谱行为分析的相关研究逐渐由基于手工特征提取的传统方法向基于深度学习的智能方法转变。然而,电磁对抗环境下通信频谱监测数据稀缺、数据不完全的问题会影响深度网络对特征的学习。同时,高动态的战场环境对分析方法实时性提出更高要求。本文聚焦电磁对抗环境下的通信频谱行为分析问题,将通信频谱行为分析相关技术的研究目标归纳为:用频行为分析、网络拓扑识别与通信意图推理3大类。阐述其内在联系,总结现有研究并梳理其发展脉络,分析面临的挑战并做出展望。
Communication spectral behavior analysis is critical to the improvement of communication situation awareness and electromagnetic reconnaissance capability in an electromagnetic countermeasure environment.With the development of artificial intelligence technology,communication spectral behavior analysis techniques have been gradually transferred from traditional methods based on feature extraction to intelligent methods based on deep learning technology.However,the insufficient and incomplete spectrum monitoring data in the electromagnetic countermeasure environment will hinder the deep network from feature learning.Moreover,the dynamic battlefield makes it even more challenging for real-time analysis.This paper categorizes the communication spectral behavior analysis technologies into three groups:Frequency behavior analysis,network topology recognition,and communication intention inference from researching objectives in the electromagnetic countermeasure environment.Furthermore,the inner relationship between the three categories is illustrated.Finally,the existing research and development venation are reviewed and prospected considering challenges.
作者
程凯欣
朱磊
杨炜伟
姚昌华
CHENG Kaixin;ZHU Lei;YANG Weiwei;YAO Changhua(College of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China;Institute of Systems Engineering,Army Academy,Beijing 100072,China;School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《数据采集与处理》
CSCD
北大核心
2022年第3期680-694,共15页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61971439,61702543)
江苏省自然科学基金(BK20191329)。
关键词
电磁对抗环境
通信频谱行为分析
用频行为分析
网络拓扑识别
通信意图推理
electromagnetic countermeasure environment
communication spectral behavior analysis
frequency behavior analysis
network topology recognition
communication intention inference
