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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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

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)
More Information
  • 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.


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