系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (1): 223-231.doi: 10.3969/j.issn.1001-506X.2021.01.27

• 通信与网络 • 上一篇    下一篇

基于认知驱动的变换域通信智能抗干扰方法

王桂胜1(), 黄国策1(), 王叶群1(), 董淑福1(), 任清华1(), 魏帅2()   

  1. 1. 空军工程大学信息与导航学院, 陕西 西安 710077
    2. 中国人民解放军95910部队, 甘肃 酒泉 735000
  • 收稿日期:2020-03-31 出版日期:2020-12-25 发布日期:2020-12-30
  • 作者简介:王桂胜(1992-),男,博士研究生,主要研究方向为军事航空通信、变换域通信。E-mail:wgsfuyun@163.com|黄国策(1962-),男,教授,博士研究生导师,博士,主要研究方向为军事航空通信、短波通信。E-mail:kgdhgc@126.com|王叶群(1993-),男,硕士研究生,主要研究方向为军事航空通信、变换域通信。E-mail:kgdwyq@126.com|董淑福(1967-),男,教授,硕士研究生导师,博士,主要研究方向为军事航空通信、变换域通信。E-mail:kgddsf@126.com|任清华(1967-),男,教授,硕士研究生导师,博士,主要研究方向为军事航空通信、变换域通信。E-mail:rentsinghua@163.com|魏帅(1992-),女,助理工程师,硕士,主要研究方向为军事航空通信、变换域通信。E-mail:960488396@qq.com
  • 基金资助:
    航天信息应用技术重点实验室高校合作(KX16260022)

Anti-interference method with intelligence for transform domain communication based on cognitive-engine

Guisheng WANG1(), Guoce HUANG1(), Yequn WANG1(), Shufu DONG1(), Qinghua REN1(), Shuai WEI2()   

  1. 1. College of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
    2. 2 Unit 95910 of the PLA, Jiuquan 735000, China
  • Received:2020-03-31 Online:2020-12-25 Published:2020-12-30

摘要:

为满足未来无人系统通信智能抗干扰的实际需要,针对传统变换域通信系统(transform domain communication system, TDCS)自身开放性有限、干扰应对能力不足等问题,设计了基于认知引擎驱动的智能系统架构,并针对各认知引擎驱动子模块提出了3种改进方法,包括基于稀疏逼近的未知干扰处理、基于稀疏表示的变换学习干扰识别以及针对性的干扰变换稀疏分析方法。实验结果表明,识别子模块与传统的分类器相比,整体的干扰识别率提高了5.2%,并且可实现无监督的学习;同时,针对典型干扰的重构精度在90%以上,实现了不同干扰类型的最优变换处理,显著提高了系统的抗干扰性能,传输误码率逼近理想水平。

关键词: 无人系统, 变换域通信, 智能抗干扰, 压缩感知, 稀疏逼近, 稀疏表示

Abstract:

In order to satisfy the actual requirements of intelligent anti-interference for unmanned aerial systems communication in the future, an intelligent system driven based on cognitive engine is designed to solve the limited openness and insufficient ability for traditional transform domain communication system (TDCS). Furthermore, three improving methods are proposed for cognitive engine-driven modules, including unknown interference approximation based on sparse approximation, transform learning interference classification based on sparse representation and target sparse analysis method for interference transformation. Simulation results show that, compared with the traditional classifier, the overall classification of interference in the classification module is increased by 5.2%, and it can be realized in an unsupervised learning. Meanwhile, the accuracy of traditional interference reconstruction is above 90%, and the optimal transformation analysis for different interference is achieved, which significantly improves the anti-interference performance of the system and makes the bit error rate of transmission close to the ideal level.

Key words: unmanned aerial system, transform domain communication, intelligent anti-interference, compressed sensing, sparse approximation, sparse representation

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