Journal of Positioning, Navigation, and Timing (J Position Navig Timing; JPNT)
Citation: Kim, S., Park, S., & Seo, J., 2023, Single Antenna Based GPS Signal Reception Condition Classification Using Machine Learning Approaches, Journal of Positioning, Navigation, and Timing, 12, 149-155.
Journal of Positioning, Navigation, and Timing (J Position Navig Timing) 2023 June, Volume 12, Issue 2, pages 149-155. https://doi.org/10.11003/JPNT.2023.12.2.149
Received on 08 May 2023, Revised on 26 May 2023, Accepted on 02 June 2023, Published on 30 June 2023.
License: Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/bync/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sanghyun Kim, Seunghyeon Park, Jiwon Seo†
School of Integrated Technology, Yonsei University, Incheon 21983, South Korea
†Corresponding Author: E-mail, jiwon.seo@yonsei.ac.kr; Tel: +82-32-749-5833 Fax: +82-32-818-5801
In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.
global positioning system, signal reception condition classification, machine learning
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Conceptualization, S. Kim, S. Park, and J. Seo; methodology, S. Kim and J. Seo; software, S. Kim and S. Park; validation, S. Park; formal analysis, S. Kim and S. Park; writing—original draft preparation, S. Kim and S. Park; writing—review and editing, J. Seo; visualization, S. Kim and S. Park.
The authors declare no conflict of interest.