A Novel Statistical Manifold Algorithm for Position Estimation

Bin Xia; Wenhao Yuan; Nan Xie; Caihong Li

doi:10.1109/JAS.2019.1911771

Volume 6 Issue 6

Nov. 2019

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(6): 1513-1518

Bin Xia, Wenhao Yuan, Nan Xie and Caihong Li, "A Novel Statistical Manifold Algorithm for Position Estimation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1513-1518, Nov. 2019. doi: 10.1109/JAS.2019.1911771

Citation:

Bin Xia, Wenhao Yuan, Nan Xie and Caihong Li, "A Novel Statistical Manifold Algorithm for Position Estimation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1513-1518, Nov. 2019. doi: 10.1109/JAS.2019.1911771

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A Novel Statistical Manifold Algorithm for Position Estimation

doi: 10.1109/JAS.2019.1911771

Funds: This work was supported by the National Natural Science Foundation of China (61701286, 61473179) and Shandong Provincial Natural Science Foundation of China (ZR2017MF047)

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Author Bio:
Bin Xia graduated from Shandong Polytechnic University, China, in 1994. He received the M.S. degree from Shandong University, China, in 2003 and the Ph.D. degree from Beijing University of Posts and Telecommunications, China, in 2006. He is currently an Associate Professor at the School of Computer Science and Technology, Shandong University of Technology, China. His research interests include signal processing and artificial intelligence

Wenhao Yuan graduated from East China University of Science and Technology, China, in 2008. He received the Ph.D. degree from East China University of Science and Technology, China, in 2013. He is currently a Lecturer at the School of Computer Science and Technology, Shandong University of Technology, China. His research interests include signal processing and machine learning, especially speech enhancement based on deep learning

Nan Xie graduated from Shandong University, China, in 1999. He received the M.S. degree from Shandong University, China, in 2002 and the Ph.D. degree from Ocean University of China, China, in 2005. He is currently an Associate Professor at the School of Computer Science and Technology, Shandong University of Technology, China. His research interests include automation and artificial intelligence, especially networked control systems, and intelligent control

Caihong Li graduated from the School of Information Engineering, Southwest University of Science and Technology, China, in 1993. She received the M.S. degree from Shandong University of Science and Technology, China, in 2000, and the Ph.D. degree from Shandong University, China, in 2007. She is currently a Professor at the School of Computer Science and Technology, Shandong University of Technology, China. Her research interests include artificial intelligence, robotics, and machine learning
Corresponding author: All the authors are with the School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China (e-mail: xiabin@sdut.edu.cn; yuanwenhao@sdut.edu.cn; xienan@sdut.edu.cn; lich@sdut.edu.cn)
Received Date: 2018-09-12
Revised Date: 2018-12-05
Accepted Date: 2019-03-02

Abstract

Abstract

In this paper, a novel statistical manifold algorithm is proposed for position estimation of sensor nodes in a wireless network, making full use of distance information available among unknown nodes and simultaneous localization of multiple unknown nodes. To begin, a ranging model including the distance information among unknown nodes is established. With the reparameterization of the natural parameter and natural statistic, the solution problem of the ranging model is transformed into a parameter estimation problem of the curved exponential family. Then, a natural gradient method is adopted to deal with the parameter estimation problem of the curved exponential family. To ensure the convergence of the proposed algorithm, a particle swarm optimization method is utilized to obtain initial values of the unknown nodes. Experimental results indicate that the proposed algorithm can improve the positioning accuracy, compared with the traditional algorithm.
- Natural gradient,
- particle swarm optimization (PSO),
- ranging model,
- statistical manifold (SM)

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A Novel Statistical Manifold Algorithm for Position Estimation

doi: 10.1109/JAS.2019.1911771

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