GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
Source abstract
Abstract
Recent advances in mobile communication require that indoor/outdoor environment information be available for both individual applications and wireless signal transmission in order to improve interference control and serve upper-layer applications. In this paper, we present a scheme to identify the indoor/outdoor environment using GPS signals combined with machine learning classification techniques. Compared to traditional schemes, which are based on received signal strength indicator (RSSI), the proposed scheme promises a robust approach with high accuracy, smooth operation when moving between indoor and outdoor environments, as well as easy implementation and training. The proposed scheme combined information from a certain number of GPS satellites, using the GPS sensor on mobile devices. Then, data are collected, preprocessed, and classified as indoor or outdoor environment using a machine learning model that is optimized for the best performance. The GPS input data were collected in the Kookmin University area and included 850 training samples and 170 test samples. The overall accuracy reached 97%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Indoor and Outdoor Localization Technologies · Speech and Audio Processing · GNSS positioning and interference
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronics Engineering, Kookmin University, Seoul 02707, Korea
Funding
Ministry of Science ICT and Future Planning
References (25)
Show source-provided references
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