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无线传感器网络中面向压缩感知定位的动态字典算法

孙保明 郭艳 李宁 张星航 李艾静

孙保明, 郭艳, 李宁, 张星航, 李艾静. 无线传感器网络中面向压缩感知定位的动态字典算法[J]. 电子与信息学报, 2017, 39(10): 2513-2519. doi: 10.11999/JEIT161379
引用本文: 孙保明, 郭艳, 李宁, 张星航, 李艾静. 无线传感器网络中面向压缩感知定位的动态字典算法[J]. 电子与信息学报, 2017, 39(10): 2513-2519. doi: 10.11999/JEIT161379
SUN Baoming, GUO Yan, LI Ning, ZHANG Xinghang, LI Aijing. Dynamic Dictionary Algorithm for CS-based Localization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2513-2519. doi: 10.11999/JEIT161379
Citation: SUN Baoming, GUO Yan, LI Ning, ZHANG Xinghang, LI Aijing. Dynamic Dictionary Algorithm for CS-based Localization in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2513-2519. doi: 10.11999/JEIT161379

无线传感器网络中面向压缩感知定位的动态字典算法

doi: 10.11999/JEIT161379
基金项目: 

国家自然科学基金(61571463, 61371124, 61472445)

Dynamic Dictionary Algorithm for CS-based Localization in Wireless Sensor Networks

Funds: 

The National Natural Science Foundation of China (61571463, 61371124, 61472445)

  • 摘要: 传统的压缩感知定位方法均假设目标准确落在某一预设的固定网格上。当目标偏离该网格,所采用的字典与真实稀疏表示字典之间存在失配,导致这些方法的定位性能大大降低。针对该问题,该文提出一种面向压缩感知定位的动态字典算法。该算法将真实稀疏表示字典建模为一个以网格为参数的动态字典,从而将定位问题转化为联合稀疏重构和参数估计问题。利用一阶泰勒展开对真实稀疏表示字典进行近似,将非凸的参数优化问题松弛为凸优化问题。仿真结果表明,相比于传统的静态字典算法,该文所提出的动态字典算法具有更好的性能。
  • LIU Yunhao, YANG Zheng, WANG Xiaoping, et al. Location, localization, and localizability[J]. Journal of Computer Science and Technology, 2010, 25(2): 274-297. doi: 10.1007/ s11390-010-9324-2
    AKYILDIZ I F, SU W, SANKARASUBRAMANIAM Y, et al. Wireless sensor networks: A survey[J]. Computer Networks, 2002, 38(4): 393-422. doi: 10.1016/S1389-1286(01)00302-4.
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109 /TIT.2006.871582.
    CANDE E J. Compressive sampling[C]. International Congress of Mathematicians, Madrid, Spain, 2006: 1433-1452.
    CEVHER V, DUARTE M, and BARANIUK R G. Distributed target localization via spatial sparsity[C]. Proceedings of the European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, 2008: 25-29.
    FENG C, VALAEE S, and TAN Z. Multiple target localization using compressive sensing[C]. IEEE Global Telecommunications Conference (GLOBECOM), Honolulu, HI, 2009: 1-6. doi: 10.1109/GLOCOM.2009.5425808.
    ZHANG B, CHEN X, ZHANG N, et al. Sparse target counting and localization in sensor networks based on compressive sensing[C]. IEEE International Conference on Computer Communications (INFOCOM), Shanghai, China, 2011: 2255-2263. doi: 10.1109/INFCOM.2011.5935041.
    何风行, 余志军, 刘海涛. 基于压缩感知的无线传感器网络多目标定位算法[J]. 电子与信息学报, 2012, 34(3): 716-721. doi: 10.3724/SP.J.1146.2011.00405.
    HE Fenghang, YU Zhijun, and LIU Haitao. Multiple target localization via compressed sensing in wireless sensor networks[J]. Journal of Electronics Information Technology, 2012, 34(3): 716-721. doi: 10.3724/SP.J.1146. 2011.00405.
    赵春晖, 许云龙, 黄辉. 基于LU分解的稀疏目标定位算法[J]. 电子与信息学报, 2013, 35(9): 2234-2239. doi: 10.3724/SP.J. 1146.2012.01527.
    ZHAO Chunhui, XU Yunlong, and HUANG Hui. Localization algorithm of sparse targets based on LU- decomposition[J]. Journal of Electronics Information Technology, 2013, 35(9): 2234-2239. doi: 10.3724/SP.J.1146. 2012.01527.
    李一兵, 黄辉, 叶方, 等. 基于奇异值分解的压缩感知定位算法[J]. 中南大学学报(自然科学版), 2014, 45(5): 1516-1521.
    LI Yibing, HUANG Hui, YE Fang, et al. Target localization via compressed sensing based on SVD[J]. Journal of Central South University (Natural Science), 2014, 45(5): 1516-1521.
    王婷婷, 柯炜, 孙超. 自适应环境变化的RSS室内定位方法[J]. 通信学报, 2014, 35(10): 210-217. doi: 10.3969/j.issn.1000- 436x.2014.10.024.
    WANG Tingting, KE Wei, and SUN Chao. Environmental- adaptive RSS-based indoor localization[J]. Journal on Communications, 2014, 35(10): 210-217. doi: 10.3969/j.issn. 1000-436x.2014.10.024.
    LIU L, CUI T, and L W. A range-free multiple target localization algorithm using compressive sensing theory in wireless sensor networks[C]. IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Philadelphia, Pennsylvania, USA, 2014: 690-695. doi: 10.1109/MASS.2014.56.
    吕伟杰, 崔婷婷, 刘超, 等. 一种新的基于压缩感知的WSN多目标定位方法[J]. 系统仿真技术, 2015, 11(1): 6-13. doi: 10.3969/j.issn.1673-1964.2015.01.002.
    L Weijie, CUI Tingting, LIU Chao, et al. A new multiple target localization based on compressed sensing theory in wsn[J]. System Simulation Technology, 2015, 11(1): 6-13. doi: 10.3969/j.issn.1673-1964.2015.01.002.
    孙保明, 郭艳, 李宁, 等. 无线传感器网络中基于压缩感知的动态目标定位算法[J]. 电子与信息学报, 2016, 38(8): 1858-1864. doi: 10.11999/JEIT151203.
    SUN Baoming, GUO Yan, LI Ning, et al. Mobile target localization algorithm using compressive sensing in wireless sensor networks[J]. Journal of Electronics Information Technology, 2016, 38(8): 1858-1864. doi: 10.11999/JEIT 151203.
    YANG Z and XIE L. A weighted atomic norm approach to spectral super-resolution with probabilistic priors[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016: 4598-4602. doi: 10.1109/ICASSP.2016.7472548.
    MISHRA K V, CHO M, KRUGER A, et al. Spectral super-resolution with prior knowledge[J]. IEEE Transactions on Signal Processing, 2015, 63(20): 5342-5357. doi: 10.1109/ TSP.2015.2452223.
    陈伟, 颜俊, 朱卫平. 利用压缩感知与多边测量技术的无线传感器网络定位算法[J]. 信号处理, 2014, 30(6): 728-735. doi: 10.3969/j.issn.1003-0530.2014.06.016.
    CHEN Wei, YAN Jun, and ZHU Weiping. Wireless sensor network location algorithm using compressive sensing and multilateral measurements[J]. Journal of Signal Processing, 2014, 30(6): 728-735. doi: 10.3969/j.issn.1003-0530.2014.06. 016.
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出版历程
  • 收稿日期:  2016-12-20
  • 修回日期:  2017-06-05
  • 刊出日期:  2017-10-19

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