| 1,274 | 24 | 122 |
| 下载次数 | 被引频次 | 阅读次数 |
认知通信对抗是一种新型的电磁作战理念,通过与人工智能技术的结合,使机器具备感知、推理、决策和评估等能力。通信对抗目标具有种类多、数量大和时变性强的特点,对抗方法也处于持续发展变化之中,针对通信对抗面临的主要问题和挑战,设计认知通信对抗系统的总体设计框架和认知引擎,对其动态感知、自主决策和效果评估等核心环节进行分析,并提出需要解决的关键技术及初步思路。
Abstract：In electromagnetic operation,cognitive communication countermeasures,when combined with artificial intelligence technology,allow the machines to have the capabilities of dynamic perception,autonomous decision-making and effect assessment.To address the large variety and quantity of targets with strong time-varying features in communication countermeasures,the countermeasure methods are also developing continuously.Suffered from the problems and challenges of communication countermeasures,the design of overall architecture and cognitive engine for a cognitive communication countermeasure system is proposed.Then,some key functions,including dynamic perception,autonomous decision-making and effect assessment,are analyzed.Finally,the key technologies and preliminary solutions are proposed.
[1] 单琳锋,金家才,张珂.电子对抗制胜机理[M].北京:国防工业出版社,2019.
[2] 美国战略与预算评估中心.决胜灰色地带——运用电磁战重获局势掌控优势[M].成都:《国际电子战》编辑部,译,2017.
[3] WHITMORE A,AGARWAL A,DA X L.The Internet of Things-A Survey of Topics and Trends[J].Information Systems Frontiers,2015,17(2):261-274.
[4] ERPEK T,SAGDUYU Y E,SHI Y.Deep Learning for Launching and Mitigating Wireless Jamming Attacks[J].IEEE Transactions on Cognitive Communications and Networking,2019,5(1):2-14.
[5] WANG Q,REN K,NING P,et al.Jamming-Resistant Multiradio Multichannel Opportunistic Spectrum Access in Cognitive Radio Networks[J].IEEE Transactions on Vehicular Technology,2016,65(10):8331-8344.
[6] WU Z L,ZHAO Y L,YIN Z D,et al.Jamming Signals Classification Using Convolutional Neural Network[C]//2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).Bilbao:IEEE,2017:62-67.
[7] XIAO L,JIANG D,XU D,et al.Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning[J].IEEE Transactions on Vehicular Technology,2018,67(10):9499-9512.
[8] LECUN Y,BENGIO Y,HINTON G.Deep Learning[J].Nature,2015,521(7553):436-444.
[9] O’SHEA T J,CORGAN J,CLANCY T C.Convolutional Radio Modulation Recognition Networks[C]//International Conference on Engineering Applications of Neural Networks.Aberdeen:EANN,2016:2-5.
[10] WEST N E,O’SHEA T.Deep Architectures for Modulation Recognition[C]//In 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).Piscataway,NJ:IEEE,2017:1-6.
[11] DAVASLIOGLU K,SAGDUYU Y E.Generative Adversarial Learning for Spectrum Sensing[C]//2018 IEEE International Conference on Communications (ICC).Kansas City:IEEE,2018:1-17.
[12] O’SHEA T J,KARRA K,CLANCY T C.Learning to Communicate:Channel Auto-encoders,Domain Specific Regularizers,and Attention[C]//2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).Limassol:IEEE,2016:2-5.
[13] QIU L,LAMARE,ZHAO R.Reduced-Rank DOA Estimation Algorithms Based on Alternating Low-Rank Decomposition[J].IEEE Signal Processing Letters,2016,23(5):565-569.
基本信息:
中图分类号:TN975;E96
引用信息:
[1]张君毅,李淳,杨勇.认知通信对抗关键技术研究[J].无线电工程,2020,50(08):619-623.
基金信息:
国家自然科学基金资助项目(U19B2028)~~
2020-06-08
2020-06-08
2020-06-08