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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018
發行者IEEE Computer Society
頁面474-479
頁數6
ISBN(電子)9781538652121
DOIs
出版狀態Published - 2018 十一月 7
事件17th International Conference on Machine Learning and Cybernetics, ICMLC 2018 - Chengdu, China
持續時間: 2018 七月 152018 七月 18

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
2
ISSN(列印)2160-133X
ISSN(電子)2160-1348

Other

Other17th International Conference on Machine Learning and Cybernetics, ICMLC 2018
國家China
城市Chengdu
期間18-07-1518-07-18

指紋

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

引用此文

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. 於 Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018 (頁 474-479). [8526984] (Proceedings - International Conference on Machine Learning and Cybernetics; 卷 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. 頁 474-479 (Proceedings - International Conference on Machine Learning and Cybernetics).
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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.",
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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. 於 Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018., 8526984, Proceedings - International Conference on Machine Learning and Cybernetics, 卷 2, IEEE Computer Society, 頁 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; 卷 2).

研究成果: Conference contribution

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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. 於 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