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

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

Fingerprint

Dilution
Neural networks
Base stations
Linear equations
Computational complexity
Communication systems

All Science Journal Classification (ASJC) codes

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

Cite this

Chen, C. S., Huan, J-F., Lin, S. C., Tseng, C. C., & Wu, C. M. (2018). Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning. In Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018 (pp. 474-479). [8526984] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2018.8526984
Chen, Chien Sheng ; Huan, Jen-Fa ; Lin, Siou Cyuan ; Tseng, Ching Chuan ; Wu, Chia Ming. / Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning. Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018. IEEE Computer Society, 2018. pp. 474-479 (Proceedings - International Conference on Machine Learning and Cybernetics).
@inproceedings{977bd5033a904c888a5f46013e5c027b,
title = "Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning",
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.",
author = "Chen, {Chien Sheng} and Jen-Fa Huan and Lin, {Siou Cyuan} and Tseng, {Ching Chuan} and Wu, {Chia Ming}",
year = "2018",
month = "11",
day = "7",
doi = "10.1109/ICMLC.2018.8526984",
language = "English",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "474--479",
booktitle = "Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018",
address = "United States",

}

Chen, CS, Huan, J-F, Lin, SC, Tseng, CC & Wu, CM 2018, Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning. in Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018., 8526984, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2, IEEE Computer Society, pp. 474-479, 17th International Conference on Machine Learning and Cybernetics, ICMLC 2018, Chengdu, China, 18-07-15. https://doi.org/10.1109/ICMLC.2018.8526984

Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning. / Chen, Chien Sheng; Huan, Jen-Fa; Lin, Siou Cyuan; Tseng, Ching Chuan; Wu, Chia Ming.

Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018. IEEE Computer Society, 2018. p. 474-479 8526984 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2).

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

TY - GEN

T1 - Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning

AU - Chen, Chien Sheng

AU - Huan, Jen-Fa

AU - Lin, Siou Cyuan

AU - Tseng, Ching Chuan

AU - Wu, Chia Ming

PY - 2018/11/7

Y1 - 2018/11/7

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85058042955&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058042955&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2018.8526984

DO - 10.1109/ICMLC.2018.8526984

M3 - Conference contribution

T3 - Proceedings - International Conference on Machine Learning and Cybernetics

SP - 474

EP - 479

BT - Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018

PB - IEEE Computer Society

ER -

Chen CS, Huan J-F, Lin SC, Tseng CC, Wu CM. Applying Geometric Dilution of Precision Approximation to Adaptive Neural Network Learning for Precise Mobile Station Positioning. In Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018. IEEE Computer Society. 2018. p. 474-479. 8526984. (Proceedings - International Conference on Machine Learning and Cybernetics). https://doi.org/10.1109/ICMLC.2018.8526984