电子学报 ›› 2018, Vol. 46 ›› Issue (9): 2068-2074.DOI: 10.3969/j.issn.0372-2112.2018.09.004

• 学术论文 • 上一篇    下一篇

基于盲自适应KLT的蒸发波导压缩感知方法

田文飚, 芮国胜, 董道广, 康健   

  1. 海军航空大学信号与信息处理山东省重点实验室, 山东烟台 264001
  • 收稿日期:2017-06-14 修回日期:2017-07-27 出版日期:2018-09-25 发布日期:2018-09-25
  • 作者简介:田文飚 男,1987年9月出生,江西南昌人.海军航空大学电子信息工程系讲师,主要研究方向为压缩感知、蒸发波导反演.E-mail:twbi5si@gmail.com;芮国胜 男,1968年3月出生,山东烟台人.海军航空大学电子信息工程系教授、博士生导师,主要研究方向为混沌通信系统及现代滤波理论.E-mail:ruigs@sina.com;董道广 男,1990年9月出生,山东济南人.海军航空大学电子信息工程系博士生,主要研究方向为Bayesian感知、蒸发波导反演;康健 女,1971年12月出生,黑龙江哈尔滨人.海军航空大学电子信息工程系副教授、硕士生导师,主要研究方向为信号处理及现代滤波理论.E-mail:kang88jian@sina.com
  • 基金资助:
    国家自然科学基金(No.41476089,No.41606117,No.61671016);"泰山学者"建设工程专项资助

Compressed Sensing of Evaporation Duct Based on Blind Adaptive KLT Estimation

TIAN Wen-biao, RUI Guo-sheng, DONG Dao-guang, KANG Jian   

  1. Signal and Information Processing Provincial Key Laboratory in Shandong, Naval Aviation University, Yantai, Shandong 264001, China
  • Received:2017-06-14 Revised:2017-07-27 Online:2018-09-25 Published:2018-09-25

摘要: 蒸发波导既可促成微波通信、雷达等系统超视距工作,又可能造成异常盲区,因此获知蒸发波导的时空态势是夺取海上制电磁权的关键.若仅靠增大传感器布设密度提升感知分辨率,则费效比高且提升空间有限.压缩感知为从相对稀少的观测数据中获知蒸发波导态势提供了可能.本文提出盲自适应KLT(Karhunen-Loéve Transform)追踪算法,通过少量观测数据,充分挖掘蒸发波导的稀疏性,准确恢复出蒸发波导的分布.理论分析和实验表明,新方法总体性能优于基于DCT(Discrete Cosine Transform)和传统KLT的对照组性能,且新方法在节省九成采样资源的前提下,最终的重构结果能够达到重构信噪比30dB的水平,为海上长时间、大范围蒸发波导态势感知提供了压缩采集的基础.

关键词: 压缩感知, 蒸发波导, 主成分分析, 稀疏表示, 信号重构, 重构算法, 匹配追踪, 无线传感器网络

Abstract: Evaporation duct helps in the over-the-horizon operating of the communication,radar systems,etc.at the microwave frequency band.In addition,it causes abnormal blind areas,too.Therefore,the evaporation duct situation acquisition is the key to seize the mastery of the electromagnetic.However,if the density of the sensor is increased to improve the sensing resolution,the cost is high and the improvement is limited.Compressed sensing (CS) provides the theoretical basis for the awareness of evaporation duct,which is recovered from a small number of low speed measurements.The blind adaptive Karhunen-Loéve transform (BAKLT) pursuit is able to fully exploit the sparsity and reconstruct the time and space situation of the evaporation duct.The analysis and simulation demonstrate that the BAKLT evaporation duct situational awareness accuracy is better than that of the control group using discrete cosine transform.The reconstructed result of the proposed method is able to reach the reconstructed SNR level of 30dB saving 90% of the sampling resources,and provides the compression basis for the full time global evaporation duct situation acquisition.

Key words: compressed sensing, evaporation duct, principal component analysis, sparse representation, signal reconstruction, reconstruction algorithm, matching pursuit, wireless sensor network

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