计算机工程 ›› 2011, Vol. 37 ›› Issue (12): 82-84.doi: 10.3969/j.issn.1000-3428.2011.12.028

• 网络与通信 • 上一篇    下一篇

无线传感器网络簇内分级数据融合算法

李海永 1,2,李 晓 1,张 岩 1   

  1. (1. 中国科学院新疆理化技术研究所,乌鲁木齐 830011;2. 中国科学院研究生院,北京 100049)
  • 收稿日期:2010-11-18 出版日期:2011-06-20 发布日期:2011-06-20
  • 作者简介:李海永(1983-),男,硕士研究生,主研方向:无线传感器网络,数据融合;李 晓,研究员、博士生导师;张 岩,副研究员

Fusion Algorithm of Hierarchical Data in Cluster for Wireless Sensor Network

LI Hai-yong 1,2, LI Xiao 1, ZHANG Yan 1   

  1. (1. Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China)
  • Received:2010-11-18 Online:2011-06-20 Published:2011-06-20

摘要: 根据无线传感器网络(WSN)资源受限的特点,在主成分分析融合方法的基础上提出一种WSN簇内分级数据融合算法。采用自学习加权方法估计各个传感器的测量方差,通过线性无偏最小方差估计法对簇内传感器节点的测量数据进行修正,用主成分分析方法得出各传感器的综合支持度和数据融合的公式。通过应用实例和仿真结果验证该方法的有效性和可靠性。

关键词: 无线传感器网络, 数据融合, 线性估计, 主成分分析,

Abstract: In order to adapt to resources-constrained Wireless Sensor Network(WSN), an improved Fusion Algorithm of Hierarchical Data in Cluster for WSN is proposed on the basis of the Principal Component Analysis(PCA). Self-learning weighted method estimates measured variance of every sensor. The linear unbiased minimum variance estimate method is adopted, which is able to reduce the errors of measured datum of the cluster sensor nodes. The formulas of comprehensive support degree of each sensor and data fusion are obtained according to the PCA method. The application example and simulation results prove that the method is effective and reliable.

Key words: Wireless Sensor Network(WSN), data fusion, linear estimation, Principal Component Analysis(PCA), cluster

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