1. College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China
2. College of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001,China
3. Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology, Nanjing, Jiangsu 210003, China
Abstract：Based on the target spatial domain discretion, the target localization problem is formulated as a sparsity-seeking problem which can be solved by compressed sensing(CS) technique. In order to satisfy the robust recovery condition for CS theory requirement, an orthogonalization preprocessing method called LU decomposition is utilized to make the measurement matrix obey the restricted isometry property(RIP).Meanwhile, an improved orthogonal matching pursuit(IOMP) algorithm is proposed to recover the position of target, and the weighted centroid algorithm to enhance the positioning precision was also adopted. The simulation results illustrate that the average localization error of the proposed algorithm is less than other existing algorithms.
［1］Viani F, Rocca P, Oliveri G, et al. Localization, tracking, and imaging of targets in wireless sensor networks: An invited review［J］. Journal of Radio Science, 2011, 46(5):1-12.
［2］Hakim K, Jayaweera S K. Source localization and tracking in a dispersive medium using wireless sensor network［J］. EURASIP Journal on Advances in Signal Processing, 2013, (1):1-19.
［3］冯晨, 张玲华. 改进混合蛙跳算法在无线传感网定位中的应用［J］. 计量学报, 2013, 34(4):360-365.
［4］Candès E J, Wakin M B. An Introduction to Compressive Sampling［J］.IEEE Signal Processing Magazine, 2008, 25(2):21-30.
［5］Qaisar S, Bilal R M, Iqbal W, et al. Compressive sensing: From theory to applications, a survey［J］. Journal of Communications and Networks, 2013,15(5):443-456.
［6］Cevher V, Duarte M, Baraniuk R. Distributed target localization via spatial sparsity［C］//Proceedings of the European Signal Processing Conference(EUSIPCO), Lausanne, Switzerland, 2008,1-5.
［7］Zhang B, Cheng X, Zhang N, et al. Sparse target counting and localization in sensor networks based on compressive sensing［C］//Proceedings of the IEEE International Conference on Computer and Communications Societies (INFOCOM’11), Shanghai, China, 2011:2255-2263.
［8］Chen Feng, Wain Sy Anthea Au, Valaee S, et al. Received signal strength based indoor positioning using compressive sensing［J］. IEEE Transactions on Mobile Computing, 2012, 11(12):1983-1993.
［9］Candes E J, Plan Y. A probabilistic and RIPless theory of compressed sensing［J］.IEEE Transactions on Information Theory, 2011,57(11):7235-7254.
［10］赵春晖, 许云龙, 黄辉. 基于LU分解的稀疏目标定位算法［J］. 电子信息学报, 2013, 35(9):2234-2239.
［11］Patwari N, Ash J N, Kyperountas S, et al. Locating the nodes: cooperative localization in wireless sensor networks［J］. IEEE Signal Processing Magazine, 2005, 22(4):54-69.
［12］卜长江, 罗跃生. 矩阵论［M］. 哈尔滨: 哈尔滨工程大学出版社, 2008: 83-85.
［13］Lin X, Lu G, Yan J, et al. Measurement matrix of compressive sensing based on Gram-Schmidt Orthogonalization ［C］// Proceedings of the Sixth IEEE International Conference on Image and Graphics(ICIG’11), Hefei, China, 2011:205-210.
［14］Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit［J］. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666.
［15］Needell D, Vershynin R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit［J］.IEEE Journal of Selected Topics in Signal Processing,2010, 4(2):310-316.
［16］Donoho D L, Tsaig Y, Drori I, et al. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit［J］. IEEE Transactions on Information Theory, 2012, 58(2): 1094-1121.
［17］Needell D, Tropp J A. Cosamp: iterative signal recovery from incomplete and inaccurate samples［J］. Communications of the ACM, 2010, 53(12): 93-100.
［18］Wang J, Urriza P, Han Y X, et al. Weighted centroid localization algorithm: theoretical analysis and distributed implementation［J］. IEEE Transactions on Wireless Communications, 2011, 10(10): 3403-3413.
2016, Vol. 37 
