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基于牛顿插值的RFID室内定位算法 被引量：2

RFID Indoor Location Based on Newton Interpolation
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摘要 随着近年无线网络的发展,出现了多种与室内定位相关的技术与应用。文中提出了一种基于牛顿插值的室内定位算法。该算法主要是基于射频识别技术(RFID)并结合Larndmarc系统利用插值求解虚拟的标签,从而对待测标签得到更精确地定位。实验证明,与k邻域算法相比,该算法在定位精度上得到了显著提高。 The rapid development of wireless network in recent years witnesses various indoor positioning related technologies and applications. This paper puts forward an indoor localization algorithm based on Newton interpola-tion. Radio frequency identification （RFID） is combined with LARNDMARC system to solve virtual label using in-terpolation, thus a more precise positioning of tags. Experiments show a significant improvement in positioning accu-racy by this algorithm over the k neighborhood algorithm.
作者 周惇 胡方明 胡华鹏 张钊
机构地区 西安电子科技大学电子工程学院
出处 《电子科技》 2013年第9期34-35,40,共3页 Electronic Science and Technology
关键词 室内定位 RFID 牛顿插值 虚拟标签 indoor location RFID Newton interpolation virtual tag
分类号 N212.9 [自然科学总论]
  • 相关文献

参考文献6

  • 1SUN Jingqi,ZHANG Zhengqi. A TOA -based indoor positio- ning system using pseudolites [ M ]. Beijing:China Academic: Journal Electronic Publishing House,2012.
  • 2蒋盛益,李庆华,李新.数据流挖掘算法研究综述[J].计算机工程与设计,2005,26(5):1130-1132. 被引量：21
  • 3JOHN H M, KURTIS D F. Numerical methods using Matlab [ M ].4版.北京:电子工业出版社,2005.
  • 4ZHAO Y Y, L1U Y H, NIL. VIRE : active RFID - based lo- calization using virtual reference elimination [ C ]. Xi'an : Par- allel Processing,2007.
  • 5ZHAO Yiyang, LIU Yunhao, LIONEL M N. VIRE: active RFID - based localization using virtual reference elimination [ C ]. Yinchuan : International Conference on Parallel Process- ing, ICPP 2007,2007 : 10 - 14,56.
  • 6NIL M, LIU Yunhao, CHO Yiu, et al. Landmarc : Indoor loca- tion sensing usingactive RF1D pervasive computing and com- munications [ C]. Soul: Proceedings of the First IEEE inter- national Conference ,2003.

二级参考文献16

  • 1Babcock B, Babu S, Datar M, et al. Models and issues in data stream systems[C]. Madison, Wisconsin, USA:Proc of ACM SIGMOD/SIGACT Conf on Princ of Database Syst. 2002.1-16.
  • 2O'Callaghan L, Mishra N, Meyerson A, et al. Streaming-data algorithms for high-quality clustering[C]. Proc of IEEE International Conference on Data Engineering, 2002.
  • 3Guha S, Mishra N, Motwani R, et al. Clustering data streams[C].Proc of IEEE Symposium on Foundations of Computer Science (FOCS'00), 2000.71-80.
  • 4Guha S, Meyerson A, Mishra N, et al. Clustering data streams:Theory and practice[J]. Knowledge and Data Engineering, IEEE Transactions, 2003, 15(3):515-528.
  • 5Giannella C, HAN Jia-wei, JIAN Pei, et al. Mining frequent patterns in data streams at multiple time granularities[C]. Proc of the NSF Workshop on Next Generation Data Mining, 2002.
  • 6Aggarwal C, Han J, Wang J, et al. A framework for clustering evolving data streams[C]. Berlin, Germany: Proc of Int Conf on Very Large Data Bases (VLDB'03), 2003.
  • 7Dora Cai Y, Clutter D, Pape G, et al. MAIDS mining alarming incidents from data streams[C].Paris, France:Proc of the 23rd ACM SIGMOD, 2004.
  • 8Dong G, Han J, LVS Lakshmanan, et al. Online mining of changes from data streams: Research problems and preliminary result [C], Proc of ACM SIGMOD Workshop on Management and Processing of Data Streams, 2003,.
  • 9Portnoy L, Eskin L, Stolfo S J. Intrusion detection with unlabeled data using clustering[C]. Proc of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001), Philadelphia,2001.
  • 10Zhang T, Ramakrishnan R. BIRCH: A efficient data clustering method for very large databases[C]. Montreal, Canada:Proc of ACM SIGMOD Conference on Management of Data, 1996.

共引文献20

同被引文献13

引证文献2

二级引证文献1

电子科技
2013年 第9期

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