Curved Exponential Family Manifold for Localization in Wireless Sensor Networks

被引：0
|
作者：
Silas M. [1 ]
Xu H. [2 ]
Song Y. [1 ]
Sun H.-F. [1 ]
机构：
[1] School of Mathematics and Statistics, Beijing Institute of Technology, Beijing
[2] School of Mathematics and Information, China West Normal University, Nanchong
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2020年 / 40卷 / 10期
关键词：
Manifold learning; Natural gradient; Optimal nonlinear estimation; Received signal strength; Statistical manifold;
D O I：
10.15918/j.tbit1001-0645.2019.121
中图分类号：
学科分类号：
摘要：
Using statistical manifold theory in information geometry and natural gradient manifold learning localization method, the self-localization problem of wireless sensor networks based on received signal strength (RSS) was studied. First, a curved exponential family localization model was constructed according to probability density function. Then, aiming at the problem of locating unknown target nodes with given initial state values, combining gradient descent method, an optimal non-linear estimation method based on this model and its improved algorithm were proposed. The good properties of gradient descent method and simulation results show that these algorithms possess better convergence effect and higher positioning accuracy. © 2020, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：1138 / 1142
页数：4
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