Manifold-based canonical correlation analysis for wireless sensor network localization
Manifold-based canonical correlation analysis for wireless sensor network localization
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DOI：
10.1002/wcm.1071
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发表时间：
2012-10
期刊：
影响因子：
--
通讯作者：
Jingjing Gu;Songcan Chen
中科院分区：
文献类型：
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作者：
Jingjing Gu;Songcan Chen
无线传感器网络中基于信号强度的位置估计是通过接收到的信号强度来确定未知传感器的物理位置。在这个领域中，很少有定位研究充分利用网络的拓扑结构，在信号空间和物理空间。本文的目标是首先建立两个有效的基于特定流形的定位模型（或局部）信号空间和物理（location）空间，并提出了相应的定位算法，称为位置估计-LPCCA（LE-LPCCA）和位置估计-LCA（LE-LCA）。由于LPCCA和LCA都相对充分地考虑了两个空间中流形结构的局部性特征，因此我们的定位算法比其他公开的先进算法具有更好的定位精度。版权所有© 2011约翰威利父子有限公司.
Signal-strength-based location estimation in wireless sensor networks is to locate the physical positions of unknown sensors via the received signal strengths. In this field, there are few localization researches sufficiently exploiting topology structures of the network in both signal space and physical space. The goal of this paper is to first establish two effective localization models based on specific manifold (or local) structures of both signal space and physical (location) space by using our previous locality preserving canonical correlation analysis (LPCCA) model and a newly-proposed locality correlation analysis (LCA) model, and then develop their corresponding novel location algorithms, called location estimation—LPCCA (LE—LPCCA) and location estimation—LCA (LE—LCA). Since both LPCCA and LCA relatively sufficiently take into account locality characteristics of the manifold structures in both the spaces, our localization algorithms developed from them consequently achieve better localization accuracy than other publicly available advanced algorithms. Copyright © 2011 John Wiley & Sons, Ltd.