期刊文献+

基于加权准则的雷达博弈波形设计

Radar Game Waveform Design Based on Weighting Criterion
下载PDF
导出
摘要 在复杂的实战环境中,雷达的先验信息具有很大的不确定性,且由于干扰更加智能化,导致实战中雷达的探测性能再度降低。为提高电子战雷达的探测性能,提出了一种基于互信息与信干噪比加权准则的雷达博弈波形设计方法。首先建立了互信息和信干噪比的加权准则,然后设计相应的雷达与干扰博弈模型,最后提出最大边缘重分配算法以解决重复博弈困境,精炼纳什均衡。仿真实验验证了该方法的有效性。 In the complex actual combat environment,the uncertainty of the prior information of the radar makes it difficult to meet the performance requirements,and the interference is more intelligent,which further reduces the detection performance of the radar in actual warfare.In order to improve the detection performance of electronic warfare radars,a radar game waveform design method based on mutual information and signal-tointerference-plus-noise ratio weighting criterion is proposed.First,weighting criterion for mutual information and signal-to-interference-plus-noise ratio is established,and then the radar and jamming game model based on the above criterion is designed.Finally,the maximum marginal reallocation algorithm is proposed to solve the repeated game dilemma and refine the Nash equilibrium.Simulation experiments verify the effectiveness of the proposed method.
作者 董军 杜晓林 崔国龙 余显祥 田团伟 DONG Jun;DU Xiaolin;CUI Guolong;YU Xianxiang;TIAN Tuanwei(School of Computer and Control Engineering,Yantai University Yantai Shandong 264005;School of Information and Communication Engineering,University of Electronic Science and Technology of China Chengdu 611731;School of Physics and Electronics,Henan University Kaifeng Henan 475001)
出处 《电子科技大学学报》 EI CAS CSCD 北大核心 2022年第6期866-874,共9页 Journal of University of Electronic Science and Technology of China
基金 国家自然科学基金(61801415,61771109)。
关键词 认知雷达 纳什均衡 STACKELBERG博弈 波形优化设计 加权准则 cognitive radar Nash equilibrium Stackelberg game waveform design weighting criterion
  • 相关文献

参考文献8

二级参考文献34

  • 1GUERCI J R.Cognitive Radar:A Knowledge-Aided Fully Adaptive Approach [C] Proceedings of IEEE Radar Conference.Washington DC:IEEE Press,2010:1365-1370.
  • 2AYKIN S.Cognitive Radar:A Way of the Future [J].IEEE Signal Processing Magazine,2006,23(1):30-40.
  • 3HAYKIN S.Cognitive Dynamic Systems [J].IEEE Signal Processing,2007,4(1):1369-1372.
  • 4GUERCI J R.Cognitive Radar:The Knowledge-Aided Fully Adaptive Approach [M].Boston/ London:Artech House,2010.
  • 5XUE Yanbo.Cognitive Radar:Theory and Simulations [D].Canada:College of Computer Engineering,McmasterUniversity,2010.
  • 6BELL M R.Information Theory and Radar Waveform Design [J].IEEE Trans on Information Theory,1993,39(5):1578-1597.
  • 7GUO D,SHAMAI S,VERDU S.Mutual Information and Minimum Mean-Square Error in Gaussian Channels [J].IEEETransactions on Information Theory,2005,51(4):1261-1282.
  • 8纠博,刘宏伟,李丽亚,吴顺君.一种基于互信息的波形优化设计方法[J].西安电子科技大学学报,2008,35(4):678-684. 被引量:19
  • 9DU Lan LIU HongWei BAO Zheng ZHANG JunYing.Radar automatic target recognition based on feature extraction for complex HRRP[J].Science in China(Series F),2008,51(8):1138-1153. 被引量:9
  • 10纠博,刘宏伟,李丽亚,吴顺君.雷达波形优化的特征互信息方法[J].西安电子科技大学学报,2009,36(1):139-144. 被引量:13

共引文献76

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部