室内定位系统中指纹库的优化方法

doi:10.3969/j.issn.1001-2400.2015.06.015

J4 ›› 2015, Vol. 42 ›› Issue (6): 81-87.doi: 10.3969/j.issn.1001-2400.2015.06.015

室内定位系统中指纹库的优化方法

马鑫迪;马建峰;高胜

(西安电子科技大学计算机学院，陕西西安 710071)

收稿日期:2014-07-10 出版日期:2015-12-20 发布日期:2016-01-25
通讯作者: 马鑫迪
作者简介:马鑫迪(1989-)，男，西安电子科技大学博士研究生，E-mail：xdma1989@163.com．
基金资助:
国家自然基金委员会-广东联合基金重点基金资助项目(U1135002)；国家自然科学基金资助项目(61303221, 61272398)；中央高校基本科研业务费专项资金资助项目(JY10000903001)

Fingerprint optimization method for the indoor localization system

MA Xindi;MA Jianfeng;GAO Sheng

(School of Computer Science and Technology, Xidian Univ., Xi'an 710071, China)

Received:2014-07-10 Online:2015-12-20 Published:2016-01-25
Contact: MA Xindi

摘要/Abstract

摘要：

指纹库的好坏直接影响到室内定位的定位精度，而现有的室内定位算法大都是建立在原始指纹库的基础之上的，缺乏对指纹库的优化处理．笔者利用k-means算法优化指纹库的构建过程，通过聚类的思想剔除已采集指纹库中的“噪点”，从而为室内定位算法提供较好的数据样本源．实验结果表明，与现有的室内定位系统相比，利用优化后的指纹库进行定位能够明显提高室内定位的精度．

关键词: 智能终端, k-means方法, 指纹库优化, 去噪

Abstract:

The accuracy of the indoor localization is influenced directly by the quality of the fingerprint. But the indoor localization algorithms in existence are almost conducted based on the original fingerprint which is not optimized. The k-means is introduced to optimize the fingerprint in this paper. And deleting the collected bad-points through the theory of cluster could make the fingerprint more accurate for the indoor localization algorithm. Compared with the indoor localization systems in existence, the result of experiments shows that the optimized fingerprint can increase the accurate of indoor localization with a higher probability.

Key words: smartphone, k-means method, optimize fingerprint, delete bad-point

马鑫迪;马建峰;高胜. 室内定位系统中指纹库的优化方法[J]. J4, 2015, 42(6): 81-87.

MA Xindi;MA Jianfeng;GAO Sheng. Fingerprint optimization method for the indoor localization system[J]. J4, 2015, 42(6): 81-87.

参考文献

［1］ Want R, Hopper A, Falcao V, et al. The Active Badge Location System ［J］. ACM Transactions on Information Systems, 1992, 10(1): 91-102.
［2］ Ward A, Jones A, Hopper A. A New Location Technique for the Active Office ［J］. IEEE Personal Communications, 1997, 4(5): 42-47.
［3］ Priyantha N B. The Cricket Indoor Location System ［D］. Cambridge: Massachusetts Institute of Technology, 2005.
［4］ Laoudias C, Constantinou G, Constantinides M, et al. The Airplace Indoor Positioning Platform for Android Smartphones ［C］//Proceedings of the IEEE 13th International Conference on Mobile Data Management. Piscataway: IEEE, 2012: 312-315.
［5］ Yang Z, Wu C, Liu Y. Locating in Fingerprint Space: Wireless Indoor Localization with Little Human Intervention ［C］//Proceedings of the 18th Annual International Conference on Mobile Computing and Networking. New York: ACM, 2012: 269-280.
［6］ Ozdenizci B, Ok K, Coskun V, et al. Development of an Indoor Navigation System Using NFC Technology ［C］//Proceedings of the 4th International Conference on Information and Computing. Piscataway: IEEE, 2011: 11-14.
［7］ Yang Z, Zhou Z, Liu Y. From RSSI to CSI: Indoor Localization via Channel Response ［J］. ACM Computing Surveys, 2013, 46(2): 25.
［8］ Liu H, Gan Y, Yang J, et al. Push the Limit of WiFi Based Localization for Smartphones ［C］//Proceedings of the 18th Annual International Conference on Mobile Computing and Networking. New York: ACM, 2012: 305-316.
［9］ Fang S H, Lin T N, Lee K C. A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments ［J］. IEEE Transactions on Wireless Communications, 2008, 7(9): 3579-3588.
［10］ Bai S, Wu T. Analysis of k-Means Algorithm on Fingerprint Based Indoor Localization System ［C］//Proceedings of the IEEE 5th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications. Piscataway: IEEE, 2013: 44-48.
［11］ Fang S H, Lin T N. Principal Component Localization in Indoor WLAN Environments ［J］. IEEE Transactions on Mobile Computing, 2012, 11(1): 100-110.
［12］ Fang S H, Wang C H. A Dynamic Hybrid Projection Approach for Improved Wi-Fi Location Fingerprinting ［J］. IEEE Transactions on Vehicular Technology, 2011, 60(3): 1037-1044.
［13］ Wagstaff K, Cardie C, Rogers S, et al. Constrained K-Means Clustering with Background Knowledge ［C］//Proceedings of the Eighteenth International Conference on Machine Learning. Williamstown: IMLS, 2001: 577-584.

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室内定位系统中指纹库的优化方法

Fingerprint optimization method for the indoor localization system

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