LIPS: Learning Based Indoor Positioning System using mobile phone-based sensors
| Autor: | Eric D. Manley, David Mascharka |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Computer science Real-time computing 020207 software engineering 02 engineering and technology 020901 industrial engineering & automation Indoor positioning system Software deployment Mobile phone Factor (programming language) 0202 electrical engineering electronic engineering information engineering Learning based computer Mobile device computer.programming_language |
| Zdroj: | CCNC |
| DOI: | 10.1109/ccnc.2016.7444919 |
| Popis: | In this paper we investigate the problem of localizing a mobile device based on readings from its sensors utilizing machine learning methodologies. We consider a real-world environment, collect a dense set of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms. We found algorithms that have a mean error as accurate as 0.76 meters, outperforming other indoor localization systems. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods. Further, we determine how less dense datasets affect accuracy, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment. The Learning Based Indoor Positioning System (LIPS) Android application source has been made available on the web. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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