Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning

Chien Sheng Chen, Jen-Fa Huan, Siou Cyuan Lin, Ching Chuan Tseng, Chia Ming Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The geometric dilution of precision is widely used as criterion for selecting the best set of the measurement devices. Some methods had been proposed to rmd the value of the Geometric Dilution of Precision (GDOP), such as using inverse matrix to solve the linear equation. But it takes a large amount of computing time to find the optimal solution. In this paper, we propose to adopt a neural network with gradient descent adaptive learning rate training algorithm to approximate the value of GDOP. The proposed two types of input/output mapping used the element in the matrix as the input data of the neural network. After finishing the training process, it can get a better approximation results with the proposed two types. The simulation result shows that proposed algorithm has lower GDOP approximation error using less Epochs than the other types. For cellular wireless communication system, it will significantly reduce the computational complexity and the training time by selecting the service base station at first with three other base stations to estimate the position of mobile station.

Original languageEnglish
Title of host publicationProceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018
PublisherIEEE Computer Society
Pages474-479
Number of pages6
ISBN (Electronic)9781538652121
DOIs
Publication statusPublished - 2018 Nov 7
Event17th International Conference on Machine Learning and Cybernetics, ICMLC 2018 - Chengdu, China
Duration: 2018 Jul 152018 Jul 18

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Other

Other17th International Conference on Machine Learning and Cybernetics, ICMLC 2018
CountryChina
CityChengdu
Period18-07-1518-07-18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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