WSN中异构数据协同的目标定位方法

doi:10.3969/j.issn.1001-2400.2014.05.033

Abstract

Abstract:

For the passive localization problem, a novel method called the heterogeneous data synergistic (HDS) based on the dynamic Bayesian network is proposed, which can coordinate heterogeneous data, and localize the target in the wireless sensor network (WSN). By comprehensively analyzing the three localization models (the signal strength model, the infrared ranging model and the particle filter) in the WSN, their characteristics are summarized in brief. According to the Bayesian network and the different characteristics, the HDS is designed under the dynamic deduction. Simulation results prove that the proposed method is adequate to the passive localization, and that compared with other traditional methods, the localization accuracy is greatly improved.

Key words: wireless sensor network, passive localization, signal strength, infrared ranging, Bayesian network

XING Tianzhang;WANG Ju;CHEN Xiaojiang;FANG Dingyi;YANG Zhe. Heterogeneous data synergistic location method in the WSN[J].J4, 2014, 41(5): 197-202.

References

［1］ Seifeldin M, Saeed A, Kosba A E, et al. Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments ［J］. IEEE Transactions on Mobile Computing, 2013, 12(7): 1321-1334.
［2］ Wang J, Fang D Y, Chen X J. LCS: Compressive Sensing based Device-Free Localization for Multiple Targets in Sensor Networks ［C］//Proceedings of IEEE International Conference on Computer Communications. Piscataway: IEEE, 2013: 145-149.
［3］ Zhang D, Ma J, Chen Q B, et al. An RF-based System for Tracking Transceiver-free Objects ［C］//Proceedings of IEEE International Conference on Pervasive Computing and Communications. Los Alamitos: IEEE, 2007: 135-144.
［4］ Zhang D, Ni L M. Dynamic Clustering for Tracking Multiple Transceiver-free Objects ［C］//Proceedings of International Conference on Pervasive Computing and Communications. Piscataway: IEEE, 2009: 1-8.
［5］ Chintalapudi K, Iyer A P, Padmanabhan V N. Indoor Localization Without the Pain ［C］//Proceedings of the Annual International Conference on Mobile Computing and Networking. New York: ACM, 2010: 173-184.
［6］ Salman N, Ghogho M, Kemp A H. On the Joint Estimation of the RSS-based Location and Path-loss Exponent［J］. IEEE Wireless Communications Letters, 2012, 1(1): 34-37.
［7］辛云宏, 杨万海. 基于伪线性卡尔曼滤波的两站红外无源定位及跟踪技术［J］. 西安电子科技大学学报, 2004, 31(4): 505-508.
Xin Yunhong, Yang Wanhai. Pseudo-linear Kalman Filter Based Passive Location and Tracking Techniques by Two Infrared Stations ［J］. Journal of Xidian University, 2004, 31(4): 505-508.
［8］ Theodore S. Rappaport, Wireless Communications: Principles and Practice［M］. London: Prentice-Hall, 1996.
［9］ Park J G, Curtis D, Teller S, et al. Implications of Device Diversity for Organic Localization ［C］//Proceedings of IEEE International Conference on Computer Communications. Piscataway: IEEE, 2011: 3182-3190.
［10］ Zhang D, Liu Y H, Guo X N, et al. RASS: A Real-time, Accurate and Scalable System for Tracking Transceiver-free Objects ［J］. IEEE Transactions on Parallel and Distributed systems, 2013, 24(5): 996-1008.

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Heterogeneous data synergistic location method in the WSN

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