High accuracy indoor positioning approach using kNN and LSTM algorithms

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Authors

  • Duong Thi Hang (Corresponding Author) Hanoi University of Industry
  • Hoang Manh Kha Hanoi University of Industry
  • Trinh Anh Vu VNU University of Engineering and Technology
  • Pham Thi Quynh Trang Hanoi University of Industry

DOI:

https://doi.org/10.54939/1859-1043.j.mst.86.2023.48-55

Keywords:

Indoor Positioning System; Machine Learning; kNN; LSTM.

Abstract

In this paper, an effective approach to improve indoor positioning accuracy using machine learning is presented. The goal of the proposed solution is to reduce the distance estimation error by combining two algorithms k Nearest Neighbor (kNN) and Long Short-Term Memory (LSTM). Simulation results show that our solution achieves an accuracy of 40% when the required error is less than 1 meter, is higher than 26% and 14%, which respectively, of other studies using machine learning on the same data set and similar simulation scenarios.

References

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[2]. Hoang, Kha and Vu, Kien, "Performance Enhancement of Wi-Fi Fingerprinting-Based IPS by Accurate Parameter Estimation of Censored and Dropped Data", Radioengineering. 28, p. 741, (2019). DOI: https://doi.org/10.13164/re.2019.0740

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[8]. Thenuardi, Dionisius and Soewito, Benfano, "Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning", Advances in Science, Technology and Engineering Systems Journal. 6, pp. 242-249, (2020). DOI: https://doi.org/10.25046/aj060127

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[10]. Zhu, Xuanyu, "Indoor Localization Based on Optimized KNN", Network and Communication Technologies. 5, p. 34, (2020). DOI: https://doi.org/10.5539/nct.v5n2p34

Published

28-04-2023

How to Cite

Duong, H., M. K. Hoàng, A. V. Trinh, and T. Phạm Thị Quỳnh. “High Accuracy Indoor Positioning Approach Using KNN and LSTM Algorithms”. Journal of Military Science and Technology, vol. 86, no. 86, Apr. 2023, pp. 48-55, doi:10.54939/1859-1043.j.mst.86.2023.48-55.

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Research Articles

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