High accuracy indoor positioning approach using kNN and LSTM algorithms
246 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.86.2023.48-55Keywords:
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
[1]. Dong, Y., Arslan, T., and Yang, Y., "An Encoded LSTM Network Model for WiFi-based Indoor Positioning", 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, (2022). DOI: https://doi.org/10.1109/IPIN54987.2022.9918116
[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
[3]. Hoang, M. K. and Haeb-Umbach, R., "Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning", 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3721-3725, (2013). DOI: https://doi.org/10.1109/ICASSP.2013.6638353
[4]. Hochreiter, Sepp and Schmidhuber, Jürgen, "Long Short-Term Memory", Neural Computation. 9(8), pp. 1735-1780, (1997). DOI: https://doi.org/10.1162/neco.1997.9.8.1735
[5]. Mosleh, Mahmood, Abd-Alhameed, Raed, and Qasim, Osama, "Indoor Positioning Using Adaptive KNN Algorithm Based Fingerprint Technique": 9th International EAI Conference, Broadnets 2018, Faro, Portugal, September 19–20, 2018, Proceedings", pp. 13-21, (2019). DOI: https://doi.org/10.1007/978-3-030-05195-2_2
[6]. Peterson, Leif, "K-nearest neighbor", Scholarpedia. 4, p. 1883, (2009). DOI: https://doi.org/10.4249/scholarpedia.1883
[7]. Roy, Priya and Chowdhury, Chandreyee, "A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems", Journal of Intelligent & Robotic Systems. 101, (2021). DOI: https://doi.org/10.1007/s10846-021-01327-z
[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
[9]. Zhang, H., et al., "Feature Fusion Using Stacked Denoising Auto-Encoder and GBDT for Wi-Fi Fingerprint-Based Indoor Positioning", IEEE Access. 8, pp. 114741-114751, (2020). DOI: https://doi.org/10.1109/ACCESS.2020.3004039
[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
