无线传感器网络中基于压缩感知的动态目标定位算法

孙保明; 郭艳; 李宁; 钱鹏

doi:10.11999/JEIT151203

无线传感器网络中基于压缩感知的动态目标定位算法

doi: 10.11999/JEIT151203

基金项目:

国家自然科学基金(61571463, 61371124, 61272487, 61472445, 61201217)

计量
- 文章访问数: 1503
- HTML全文浏览量: 99
- PDF下载量: 602
- 被引次数: 0
出版历程
- 收稿日期: 2015-10-29
- 修回日期: 2016-03-29
- 刊出日期: 2016-08-19

Mobile Target Localization Algorithm Using Compressive Sensing in Wireless Sensor Networks

Funds:

The National Natural Science Foundation of China (61571463, 61371124, 61272487, 61472445, 61201217)

摘要

摘要: 传统的动态目标定位算法需要采集、存储和处理大量数据，并不适用于能量受限的无线传感器网络。针对该缺陷，该文提出一种基于压缩感知的动态目标定位算法。该算法利用目标的运动规律设计稀疏表示基，从而将动态目标定位问题转化为稀疏信号恢复问题。针对传统观测矩阵难以实现的缺陷，该算法设计可实现且与稀疏表示基相关性低的稀疏观测矩阵，从而保证了算法的重构性能。该算法的特点是可利用较少的数据采集实现动态目标定位，从而大大延长无线传感器网络的寿命。仿真结果表明，该文所提出的基于压缩感知的动态目标定位算法具有较好的定位性能。
- 无线传感器网络 /
- 动态目标定位 /
- 压缩感知 /
- 稀疏表示基 /
- 观测矩阵
Abstract: Traditional mobile target localization algorithms are not suitable for wireless sensor networks as they need to collect, store, and process a mass of data. To address this issue, a mobile target localization algorithm based on compressive sensing is proposed. Two sparse representation bases are designed by exploiting the movement characteristics of mobile targets, therefore the mobile target localization issue is transferred into a sparse signal recovery issue. To avoid the unpractical limitation of traditional measurement matrices, two sparse measurement matrices are proposed that are practical and lowly coherent with the designed representation bases. The characteristic of this algorithm is that mobile target localization can be achieved by collecting a little data, thus prolonging the lifetime of wireless sensor networks. Simulation results indicate that the proposed localization algorithm based on compressive sensing is highly efficient.
- Wireless sensor networks /
- Mobile target localization /
- Compressive sensing /
- Sparse representation basis /
- Measurement matrix

HTML全文

参考文献(23)

AKYILDIZ I F, SU W, SANKARASUBRAMANIAM Y, et al. Wireless sensor networks: a survey[J]. Computer Networks, 2002, 38(4): 393-422.

LIU Y, YANG Z, WANG X, et al. Location, localization, and localizability[J]. Journal of Computer Science and Technology, 2010, 25(2): 274-297.

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 12891306.

CAND 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.

CHEN F, VALAEE S, and TAN Z. Multiple target localization using compressive sensing[C]. IEEE Global Telecommunications Conference (GLOBECOM), Honolulu, HI, USA, 2009: 1-6.

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.

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 Tevhnology, 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 Tevhnology, 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.

GUYEN G K, VAN NGUYEN T, and SHIN H. Learning dictionary and compressive sensing for WLAN localization[C]. IEEE Wireless Communications and Networking Conference (WCNC), Istanbul, Turkey, 2014: 2910-2915.

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.

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.

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.

WANG Tingting, KE Wei, and SUN Chao. Environmental- adaptive RSS-based indoor localization[J]. Journal on Communications, 2014, 35(10): 210-217.

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, PA, USA, 2014: 690-695.

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.

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.

SONG C and XIA S. Sparse signal recovery by minimization under restricted isometry property[J]. IEEE Signal Processing Letters, 2014, 21(9): 1154-1158.

WU X and LIU M. In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing[C]. Proceedings of the 11th ACM International Conference on Information Processing in Sensor Networks, Beijing, China, 2012: 1-12.

施引文献

资源附件(0)

访问统计