Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms
The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is st...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716661953 |
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| _version_ | 1832547230817452032 |
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| author | Jesus Martínez-Gómez Miguel Martínez del Horno Manuel Castillo-Cara Víctor Manuel Brea Luján Luis Orozco Barbosa Ismael García-Varea |
| author_facet | Jesus Martínez-Gómez Miguel Martínez del Horno Manuel Castillo-Cara Víctor Manuel Brea Luján Luis Orozco Barbosa Ismael García-Varea |
| author_sort | Jesus Martínez-Gómez |
| collection | DOAJ |
| description | The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms. |
| format | Article |
| id | doaj-art-73ac9d7d41d94a5eb0b4c47bfc37af61 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-73ac9d7d41d94a5eb0b4c47bfc37af612025-02-03T06:45:37ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-08-011210.1177/1550147716661953Spatial statistical analysis for the design of indoor particle-filter-based localization mechanismsJesus Martínez-Gómez0Miguel Martínez del Horno1Manuel Castillo-Cara2Víctor Manuel Brea Luján3Luis Orozco Barbosa4Ismael García-Varea5Albacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Albacete, SpainAlbacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Albacete, SpainSchool of Computer Science, Faculty of Science, Universidad Nacional de Ingeniería, Lima, PerúAlbacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Albacete, SpainAlbacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Albacete, SpainAlbacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Albacete, SpainThe accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.https://doi.org/10.1177/1550147716661953 |
| spellingShingle | Jesus Martínez-Gómez Miguel Martínez del Horno Manuel Castillo-Cara Víctor Manuel Brea Luján Luis Orozco Barbosa Ismael García-Varea Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms International Journal of Distributed Sensor Networks |
| title | Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms |
| title_full | Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms |
| title_fullStr | Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms |
| title_full_unstemmed | Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms |
| title_short | Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms |
| title_sort | spatial statistical analysis for the design of indoor particle filter based localization mechanisms |
| url | https://doi.org/10.1177/1550147716661953 |
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