期刊文献+

马尔可夫模型于无线信道异常检测中的应用 被引量:10

Application of markov model in wireless channel anomaly detection
原文传递
导出
摘要 无线信道异常检测中,现有基于大尺度衰落建模的能量检测法简便、迅速,然而其在检测过程中忽略了阴影衰落的实时、随机变化的特性。马尔可夫模型在无线信道建模中具有良好的应用前景,能够有效地应用于阴影衰落的动态分析。通过统计分析先验马尔可夫模型矩阵的相似度变化阈值,计算先验与实时马尔可夫模型矩阵相似度,检测阴影衰落的变化规律是否发生变化,实现无线信道环境的异常检测。该方法作为大尺度衰落建模能量检测法的补充,能够完善检测覆盖面,提高检测的准确率。多次仿真实验结果表明,在高斯白噪声入侵时,该方法可实现准确的检测。 Among wireless channel abnormal detection methods,the way of constructing large scale fading model for energy detection is easy and fast.However,this method has ignored shadow fading which has characteristics of real-time and random changing.Using Markov model as a way to analyze random process has good application prospects in wireless channel modeling.It can be effectively used to analyze the change of shadow fading.First,the threshold of the prior Markov model matrix’s similarity should be statistically analyzed.Then,the similarity between the prior and the real-time Markov model matrix is calculated.Whether the regular pattern of shadow fading has changed can be found to compare the two similarities mentioned above,then the abnormality of wireless channel environment detection has finished.Plenty of experimental results based on the simulation show that this method can achieve accurate detection for the Gaussian white noise intrusions.
作者 袁莉芬 郭涛 何怡刚 吕密 程珍 索帅 Yuan Lifen;Guo Tao;He Yigang;Lu Mi;Cheng Zhen;Suo Shuai(School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009,China;Texas A&M University,College Station,Texas TX 77843,USA)
出处 《电子测量与仪器学报》 CSCD 北大核心 2019年第3期29-34,共6页 Journal of Electronic Measurement and Instrumentation
基金 国家重点研发计划"重大科学仪器设备开发"(2016YFF0102200) 国家自然科学基金重点项目(51637004)资助
关键词 无线信道 异常检测 大尺度衰落模型 能量检测 马尔可夫模型 wireless channel abnormal detection large scale fading model energy detection Markov model
  • 相关文献

参考文献9

二级参考文献78

  • 1刘奎学,陈丽华,吕清华,全宝富.无线有毒气体浓度采集系统[J].仪器仪表学报,2006,27(z2):1302-1304. 被引量:6
  • 2康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004,28(17):1-11. 被引量:499
  • 3何子述,韩春林,刘波.MIMO雷达概念及其技术特点分析[J].电子学报,2005,33(B12):2441-2445. 被引量:97
  • 4张冰,孔锐.一种支持向量机的组合核函数[J].计算机应用,2007,27(1):44-46. 被引量:22
  • 5DUAN Qing, ZHAO Jianguo, NIU Lin, et al. Recession based on sparse bayesian learning and the applications in electric systems [ C ]//Fourth International Conference on Natural Computing, October 18-20, 2008, Jinan, China. 2008:106-111.
  • 6段青,赵建国,马艳.相关向量机与支持向量机在短期电力负荷预测中的比较[C]//全国电气工程博士论坛,成都:西南交通大学出版社,2008:314-319.
  • 7YU Weimiao, DU Tiehua, LIM Kahbin. Comparison of the support vector machine and relevant vector machine in regression and classification problems[ C ]//8th International Conference on Control, Automation, Robotics and Vision, December 6 -9, 2004, Kunming, China. 2004, 2:1309-1314.
  • 8BOWD C, MEDEIROS F A, ZHANG Zuobua, et al. Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements [J]. Insestigative Ophthalmology & Visual ,Science, 2005,46(4) : 1322 - 1329.
  • 9STEINWART I. On the influence of the kernel on the generalization ability of support vector machines [ J ]. Journal of Machine Learning Research, 2001,2( 3 ):67-93.
  • 10VAPNIK V N. The Nature of Statistical Learning Theory [ M ]. New York : Springer-Verlag, 1995 : 11 - 13.

共引文献125

同被引文献95

引证文献10

二级引证文献21

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