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基于贝叶斯压缩感知的周跳探测与修复方法

李慧 赵琳 李亮

基于贝叶斯压缩感知的周跳探测与修复方法

李慧, 赵琳, 李亮
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  • 针对观测噪声对周跳探测与修复性能的影响,提出了一种新的利用贝叶斯压缩感知技术进行周跳探测与修复的方法.在历元间-站间载波相位双差观测模型的基础上,通过挖掘周跳信号的稀疏特性,获取感知矩阵,推导并建立稀疏周跳探测模型,利用稀疏贝叶斯学习中的相关向量机原理,结合周跳相关数据的先验信息,基于主动相关决策理论,进行回归估计获得周跳预测值的分布,进而实现周跳的探测与修复.实验表明,新方法在仅利用单频或双频载波相位观测量的情况下能有效探测并修复周跳,性能优于正交匹配追踪法及l1范数法.
      通信作者: 李慧, lihuiheu@hotmail.com
    • 基金项目: 国家自然科学基金(批准号:61273081)、国家自然科学基金青年基金(批准号:61304235,61401114)、中央高校基本科研业务费专项资金(批准号:HEUCFD1431)和国家留学基金资助的课题.
    [1]

    Cai C S, Liu Z Z, Xia P F, Dai W J 2013 GPS Solutions 17 247

    [2]

    Parkins A 2011 GPS Solutions 15 391

    [3]

    Ji S Y, Wang Z J, Chen W, Weng D J, Xu Y, Fan S J, Huang B H, Sun G Y, Wang H Q, He Y W 2014 Survey Rev. 46 104

    [4]

    Xu G C 2007 GPS:Theory, Algorithms and Applications (Vol. 2) (Berlin:Springer Science & Business Media) p167

    [5]

    Dai Z, Knedlik S, Loffeld O 2008 Proceedings of 5th Workshop on Positioning, Navigation and Communication Hannover, Germany, March 27-27, 2008 p37

    [6]

    Dai Z 2012 GPS Solutions 16 267

    [7]

    Liu Z Z 2011 J. Geodesy. 85 171

    [8]

    Henkel P, Oku N 2015 International Association of Geodesy Symposia 142 291

    [9]

    De Lacy M C, Reguzzoni M, Sansò F 2012 GPS Solutions 16 353

    [10]

    Zhao Q L, Sun B Z, Dai Z Q, Hu Z G, Shi C, Liu J N 2015 GPS Solutions 19 381

    [11]

    Yao Y F, Gao J X, Wang J, Hu H, Li Z K 2016 Survey Rev. 48 367

    [12]

    Sun B Q, Ou J K, Sheng C Z, Liu J H 2010 Geomat. Inform. Sci. Wuhan Univ. 10 1157 (in Chinese)[孙保琪, 欧吉坤, 盛传贞, 刘吉华2010武汉大学学报 10 1157]

    [13]

    Rapoport L 2014 ION GNSS2014 Tampa, USA, September 8-12, 2014 p2602

    [14]

    Gao Y, Huang G Y, Zhang X H, Xu H W, Zhang L Q 2015 The 27th Chinese Control and Decision Conference Qingdao, China, May 23-25, 2015 p3627

    [15]

    Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289

    [16]

    Candes E J, Romberg J K, Tao T 2006 Commun. Pure and Appl. Math. 59 1207

    [17]

    Duarte M F, Baraniuk R G 2013 Appl. Comput. Harmon. Anal. 35 111

    [18]

    Foucart S, Rauhut H 2013 A Mathematical Introduction to Compressive Sensing (Vol. 1) (New York:Springer) p61

    [19]

    Leick A, Rapoport L, Tatarnikov D J 2015 GPS Satellite Surveying (Vol. 4) (New Jersey:John Wiley & Sons) p681

    [20]

    Sharma A, Paliwal K K, Imoto S, Miyano S 2013 Int. J. Mach. Learn. Cyb. 4 679

    [21]

    Kang R Z, Tian P W, Yu H Y 2014 Acta Phys. Sin. 63 200701 (in Chinese)[康荣宗, 田鹏武, 于宏毅2014物理学报 63 200701]

    [22]

    Candès E J, Romberg J, Tao T 2006 IEEE Trans. Inform. Theory 52 489

    [23]

    Candès E J 2008 Comptes Rendus Mathematique 346 589

    [24]

    Wen F Q, Zhang G, Fen D 2015 Acta Phys. Sin. 64 070201 (in Chinese)[文方青, 张弓, 贲德2015物理学报 64 070201]

    [25]

    Tropp J A, Gilbert A C 2007 IEEE Trans. Inform. Theory 53 4655

    [26]

    Ji S H, Xue Y, Carin L 2008 IEEE Trans. Signal Process. 56 2346

    [27]

    Wipf D P, Rao B D 2004 IEEE Trans. Signal Process. 52 2153

  • [1]

    Cai C S, Liu Z Z, Xia P F, Dai W J 2013 GPS Solutions 17 247

    [2]

    Parkins A 2011 GPS Solutions 15 391

    [3]

    Ji S Y, Wang Z J, Chen W, Weng D J, Xu Y, Fan S J, Huang B H, Sun G Y, Wang H Q, He Y W 2014 Survey Rev. 46 104

    [4]

    Xu G C 2007 GPS:Theory, Algorithms and Applications (Vol. 2) (Berlin:Springer Science & Business Media) p167

    [5]

    Dai Z, Knedlik S, Loffeld O 2008 Proceedings of 5th Workshop on Positioning, Navigation and Communication Hannover, Germany, March 27-27, 2008 p37

    [6]

    Dai Z 2012 GPS Solutions 16 267

    [7]

    Liu Z Z 2011 J. Geodesy. 85 171

    [8]

    Henkel P, Oku N 2015 International Association of Geodesy Symposia 142 291

    [9]

    De Lacy M C, Reguzzoni M, Sansò F 2012 GPS Solutions 16 353

    [10]

    Zhao Q L, Sun B Z, Dai Z Q, Hu Z G, Shi C, Liu J N 2015 GPS Solutions 19 381

    [11]

    Yao Y F, Gao J X, Wang J, Hu H, Li Z K 2016 Survey Rev. 48 367

    [12]

    Sun B Q, Ou J K, Sheng C Z, Liu J H 2010 Geomat. Inform. Sci. Wuhan Univ. 10 1157 (in Chinese)[孙保琪, 欧吉坤, 盛传贞, 刘吉华2010武汉大学学报 10 1157]

    [13]

    Rapoport L 2014 ION GNSS2014 Tampa, USA, September 8-12, 2014 p2602

    [14]

    Gao Y, Huang G Y, Zhang X H, Xu H W, Zhang L Q 2015 The 27th Chinese Control and Decision Conference Qingdao, China, May 23-25, 2015 p3627

    [15]

    Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289

    [16]

    Candes E J, Romberg J K, Tao T 2006 Commun. Pure and Appl. Math. 59 1207

    [17]

    Duarte M F, Baraniuk R G 2013 Appl. Comput. Harmon. Anal. 35 111

    [18]

    Foucart S, Rauhut H 2013 A Mathematical Introduction to Compressive Sensing (Vol. 1) (New York:Springer) p61

    [19]

    Leick A, Rapoport L, Tatarnikov D J 2015 GPS Satellite Surveying (Vol. 4) (New Jersey:John Wiley & Sons) p681

    [20]

    Sharma A, Paliwal K K, Imoto S, Miyano S 2013 Int. J. Mach. Learn. Cyb. 4 679

    [21]

    Kang R Z, Tian P W, Yu H Y 2014 Acta Phys. Sin. 63 200701 (in Chinese)[康荣宗, 田鹏武, 于宏毅2014物理学报 63 200701]

    [22]

    Candès E J, Romberg J, Tao T 2006 IEEE Trans. Inform. Theory 52 489

    [23]

    Candès E J 2008 Comptes Rendus Mathematique 346 589

    [24]

    Wen F Q, Zhang G, Fen D 2015 Acta Phys. Sin. 64 070201 (in Chinese)[文方青, 张弓, 贲德2015物理学报 64 070201]

    [25]

    Tropp J A, Gilbert A C 2007 IEEE Trans. Inform. Theory 53 4655

    [26]

    Ji S H, Xue Y, Carin L 2008 IEEE Trans. Signal Process. 56 2346

    [27]

    Wipf D P, Rao B D 2004 IEEE Trans. Signal Process. 52 2153

  • 引用本文:
    Citation:
计量
  • 文章访问数:  1498
  • PDF下载量:  295
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-05-23
  • 修回日期:  2016-08-23
  • 刊出日期:  2016-12-05

基于贝叶斯压缩感知的周跳探测与修复方法

  • 1. 哈尔滨工程大学自动化学院, 哈尔滨 150001
  • 通信作者: 李慧, lihuiheu@hotmail.com
    基金项目: 

    国家自然科学基金(批准号:61273081)、国家自然科学基金青年基金(批准号:61304235,61401114)、中央高校基本科研业务费专项资金(批准号:HEUCFD1431)和国家留学基金资助的课题.

摘要: 针对观测噪声对周跳探测与修复性能的影响,提出了一种新的利用贝叶斯压缩感知技术进行周跳探测与修复的方法.在历元间-站间载波相位双差观测模型的基础上,通过挖掘周跳信号的稀疏特性,获取感知矩阵,推导并建立稀疏周跳探测模型,利用稀疏贝叶斯学习中的相关向量机原理,结合周跳相关数据的先验信息,基于主动相关决策理论,进行回归估计获得周跳预测值的分布,进而实现周跳的探测与修复.实验表明,新方法在仅利用单频或双频载波相位观测量的情况下能有效探测并修复周跳,性能优于正交匹配追踪法及l1范数法.

English Abstract

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