Hierarchical Greedy Matching Pursuit for Multi-target Localization in Wireless Sensor Networks Using Compressive Sensing
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摘要: 针对基于压缩感知（Compressive sensing，CS）的多目标定位问题，通过分析多目标场景中的隐含结构信息，本文提出一种层级的贪婪匹配追踪定位算法.该算法首先获得多目标在网格化空间中的可能位置作为全局估计层，然后利用该全局估计信息作为稀疏恢复层的输入信息，在网格化空间中重构多目标位置矢量.本文证明了文献中广泛采用的基于正交化的预处理方式实质上降低了信噪比（Signal to noise ratio，SNR），从而降低了定位性能.本文通过全局估计，预先排除了不可能的位置，等效于从观测子空间中分离出信号子空间，从而降低了观测噪声的影响.通过理论分析与计算机仿真，表明所提算法具有线性复杂度且在相同信噪比下具有更高的定位正确率和定位精度.Abstract: This paper addresses the problem of multi-target localization in wireless sensor networks using compressive sensing (CS). We first analyze the ample implicit structured information contained in the multi-target localization scenario. Then, a hierarchical greedy matching pursuit (HGMP) approach for multi-target localization is proposed. In the proposed HGMP algorithm, the possible positions of targets in the meshing space are obtained as the global estimation layer, and subsequently the global estimation information is used as the input information to the sparse recovery layer to reconstruct the multi-target localization vector in the meshing space. Moreover, we prove that the orthogonality-based preprocessing operation widely adopted in the literature reduces the signal-to-noise ratio (SNR), degrades the localization performance, a problem that has never been addressed before. Through the global estimation layer, impossible positions are preliminarily removed, which is equivalent to separating the signal subspace from the observation subspace, thus reducing the influence of the observed noise. Finally, theoretical analysis and computer simulations show that the proposed algorithm enjoys a linear computational complexity and a higher localization accuracy at the same SNR.1) 本文责任编委 吴立刚
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[1] Suo H, Wan J F, Huang L, Zou G F. Issues and challenges of wireless sensor networks localization in emerging applications. In:Proceedings of the 2012 IEEE International Conference on Computer Science and Electronics Engineering. Hangzhou, China:IEEE, 2012. 447-451 https://www.researchgate.net/publication/261315118_Issues_and_Challenges_of_Wireless_Sensor_Networks_Localization_in_Emerging_Applications [2] Goldoni E, Savioli A, Risi M, Gamba P. Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4. In:Proceedings of the 2010 European Wireless Conference. Lucca, Italy:IEEE, 2010. 71-77 [3] 罗旭, 柴利, 杨君.无线传感器网络下静态水体中的近岸污染源定位.自动化学报, 2014, 40 (5):849-861 http://www.aas.net.cn/CN/abstract/abstract18353.shtmlLuo Xu, Chai Li, Yang Jun. Offshore pollution source localization in static water using wireless sensor networks. Acta Automatica Sinica, 2014, 40 (5):849-861 http://www.aas.net.cn/CN/abstract/abstract18353.shtml [4] 彭宇, 罗清华, 王丹, 彭喜元.基于区间数聚类的无线传感器网络定位方法.自动化学报, 2012, 38 (7):1190-1199 http://www.aas.net.cn/CN/abstract/abstract13552.shtmlPeng Yu, Luo Qing-Hua, Wang Dan, Peng Xi-Yuan. WSN localization method using interval data clustering. Acta Automatica Sinica, 2012, 38 (7):1190-1199 http://www.aas.net.cn/CN/abstract/abstract13552.shtml [5] Liu D X, Kuang Y J, Wei W. Research and improvement of DVHOP localization algorithm in wireless sensor networks. In:Proceedings of the 2010 International Conference on Computational Problem-Solving. Lijiang, China:IEEE, 2010. 47-50 https://www.researchgate.net/publication/308733981_Research_and_improvement_of_DV_HOP_localization_algorithm_in_wireless_sensor_networks [6] 杨小军.多跳无线传感器网络下信道感知的目标定位方法.自动化学报, 2013, 39 (7):1110-1116 http://www.aas.net.cn/CN/abstract/abstract18139.shtmlYang Xiao-Jun. Channel aware target localization in multi-hop wireless sensor networks. Acta Automatica Sinica, 2013, 39 (7):1110-1116 http://www.aas.net.cn/CN/abstract/abstract18139.shtml [7] Bahl P, Padmanabhan V N. RADAR:an in-building RF-based user location and tracking system. In:Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies INFOCOM. Tel Aviv, Israel:IEEE, 2000. 775-784 https://www.researchgate.net/publication/3842777_RADAR_An_in-building_RF-based_user_location_and_tracking_system [8] Zhang W, Liu K, Zhang W D, Zhang Y M, Gu J. Wi-Fi positioning based on deep learning. In:Proceedings of the 2014 IEEE International Conference on Information and Automation. Hailar, China:IEEE, 2014. 1176-1179 https://www.researchgate.net/publication/286573524_Wi-Fi_positioning_based_on_deep_learning [9] Candes E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, 25 (2):21-30 doi: 10.1109/MSP.2007.914731 [10] Feng C, Valaee S, Tan Z H. Multiple target localization using compressive sensing. In:Proceedings of the 2009 IEEE Global Telecommunications Conference. Honolulu, HI, USA:IEEE, 2009. 1-6 https://www.researchgate.net/publication/221287682_Multiple_Target_Localization_Using_Compressive_Sensing [11] Zhang B W, Cheng X Z, Zhang N, Cui Y, Li Y S, Liang Q L. Sparse target counting and localization in sensor networks based on compressive sensing. In:Proceedings of the 2011 IEEE INFOCOM. Shanghai, China:IEEE, 2011. 2255-2263 https://www.researchgate.net/publication/224243922_Sparse_target_counting_and_localization_in_sensor_networks_based_on_compressive_sensing [12] Cevher V, Duarte M F, Baraniuk R G. Distributed target localization via spatial sparsity. In:Proceedings of the 6th European Signal Processing Conference. Lausanne, Switzerland:IEEE, 2008. 1-5 https://www.researchgate.net/publication/46146266_Distributed_target_localization_via_spatial_sparsity [13] Cevher V, Boufounos P, Baraniuk R G, Gilbert A C, Strauss M J. Near-optimal Bayesian localization via incoherence and sparsity. In:Proceedings of the 2009 International Conference on Information Processing in Sensor Networks. San Francisco, CA, USA:IEEE, 2009. 205-216 https://www.researchgate.net/publication/46146271_Near-Optimal_Bayesian_Localization_via_Incoherence_and_Sparsity [14] Lin X F, You K Y, Guo W B. Delaunay triangulation and mesh grid combining algorithm for multiple targets localization using compressive sensing. In:Proceedings of the 19th International Symposium on Wireless Personal Multimedia Communications. Shenzhen, China:IEEE, 2017. 25-30 https://ieeexplore.ieee.org/document/7954448/ [15] Candés E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurement. Communications on Pure and Applied Mathematics, 2006, 59 (8):1207-1223 doi: 10.1002/(ISSN)1097-0312 [16] Yang S X, Guo Y, Liu X, Niu D W, Sun B M. A localization algorithm based on compressive sensing by K-nearest neighbor classification. In:Proceedings of the 13th International Conference on Signal Processing. Chengdu, China:IEEE, 2017. 863-867 https://www.researchgate.net/publication/315512511_A_localization_algorithm_based_on_compressive_sensing_by_K-nearest_Neighbor_classification [17] Needell D, Vershynin R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. IEEE Journal of Selected Topics in Signal Processing, 2010, 4 (2), 310-316 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0223736356/ [18] Wang Y, Feng S L, Zhang P. Cluster-based multi-target localization under partial observations. In:Proceedings of the 81st Vehicular Technology Conference. Glasgow, UK:IEEE, 2015. 1-6 https://www.researchgate.net/publication/283103190_Cluster-Based_Multi-Target_Localization_under_Partial_Observations -
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