Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression
Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor vis...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2076-3417/9/6/1048 |
| _version_ | 1818041696178405376 |
|---|---|
| author | Huy Q. Tran Cheolkeun Ha |
| author_facet | Huy Q. Tran Cheolkeun Ha |
| author_sort | Huy Q. Tran |
| collection | DOAJ |
| description | Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy. |
| first_indexed | 2024-12-10T08:34:31Z |
| format | Article |
| id | doaj.art-488078c8f1b74b9fab10d13eeed33438 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T08:34:31Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-488078c8f1b74b9fab10d13eeed334382022-12-22T01:56:01ZengMDPI AGApplied Sciences2076-34172019-03-0196104810.3390/app9061048app9061048Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and RegressionHuy Q. Tran0Cheolkeun Ha1Robotics and Mechatronics Lab, University of Ulsan, Ulsan 44610, KoreaRobotics and Mechatronics Lab, University of Ulsan, Ulsan 44610, KoreaRecently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.http://www.mdpi.com/2076-3417/9/6/1048indoor positioning systemvisible lightmachine learning classificationmachine learning regressionmultipath reflectionssignal pre-processing |
| spellingShingle | Huy Q. Tran Cheolkeun Ha Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression Applied Sciences indoor positioning system visible light machine learning classification machine learning regression multipath reflections signal pre-processing |
| title | Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression |
| title_full | Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression |
| title_fullStr | Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression |
| title_full_unstemmed | Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression |
| title_short | Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression |
| title_sort | improved visible light based indoor positioning system using machine learning classification and regression |
| topic | indoor positioning system visible light machine learning classification machine learning regression multipath reflections signal pre-processing |
| url | http://www.mdpi.com/2076-3417/9/6/1048 |
| work_keys_str_mv | AT huyqtran improvedvisiblelightbasedindoorpositioningsystemusingmachinelearningclassificationandregression AT cheolkeunha improvedvisiblelightbasedindoorpositioningsystemusingmachinelearningclassificationandregression |