Developing an Improved Fingerprint Positioning Radio Map using the K-Means Clustering Algorithm
| Autor: | Sang Gu Lee, Chae-Woo Lee |
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| Rok vydání: | 2020 |
| Předmět: |
Matching (statistics)
Computer science business.industry Fingerprint (computing) k-means clustering Process (computing) 020302 automobile design & engineering 020206 networking & telecommunications Pattern recognition 02 engineering and technology Signal 0203 mechanical engineering Sampling (signal processing) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Cluster analysis business |
| Zdroj: | ICOIN |
| DOI: | 10.1109/icoin48656.2020.9016627 |
| Popis: | Recently, with the development of Wi-Fi technology and the increase of mobile devices, location-based services that provide user location have drawn much attention. One of the most utilized methods for an indoor-based location acquisition system is the fingerprinting matching method, which estimates the user's location by analyzing the strength of the Wi-Fi signal. This system, however, suffers from the RSS variance problem in which the signal strength is unstable due to environmental factors. Therefore, it is crucial to collect stable sample records, which can be achieved by collecting signal samples over a sufficient period and averaging them. However, this is not the most suitable solution since signal strengths tend to be reliant on the device used and the time that it was measured. Eventually, sampled signals tend to form groups of clusters with respect to their obtained attributes. In this paper, we propose a more accurate radio map-generating algorithm by finding out the optimal number of clusters and applying the K-means clustering algorithm. This process generates a more precise radio map than the average sampling model. |
| Databáze: | OpenAIRE |
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