An improved geometric algorithm for indoor localization
Indoor localization system using receive signal strength indicator from wireless access point has attracted lots of attention recently. Geometric method is one of the most widely used spatial graph algorithms to locate object in an indoor environment, but it does not achieve good results when it is...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718767376 |
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| _version_ | 1832547875876241408 |
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| author | Junhua Yang Yong Li Wei Cheng |
| author_facet | Junhua Yang Yong Li Wei Cheng |
| author_sort | Junhua Yang |
| collection | DOAJ |
| description | Indoor localization system using receive signal strength indicator from wireless access point has attracted lots of attention recently. Geometric method is one of the most widely used spatial graph algorithms to locate object in an indoor environment, but it does not achieve good results when it is applied to a limited amount of valid data, especially when using the trilateration method. On the other hand, localization based on fingerprint can achieve high accuracy but need to pay heavy manual labor for fingerprint database establishment. In this article, we propose a bilateral greed iteration localization method based on greedy algorithm in order to use all of the effective anchor points. Comparing to trilateration, fingerprint, and maximum-likelihood method, the bilateral greed iteration method improves the localization accuracy and reduces complexity of localization process. The method proposed, coupled with measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms trilateration and maximum-likelihood receive signal strength indicator–based indoor location methods without using any radio map information nor a complicated algorithm. Extensive experiment results in a Wi-Fi coverage office environment indicate that the proposed bilateral greed iteration method reduces the localization error, 63.55%, 9.93%, and 47.85%, compared to trilateration, fingerprint, and maximum-likelihood method, respectively. |
| format | Article |
| id | doaj-art-ce374b1648a643d0b98f8c57a9cda93d |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ce374b1648a643d0b98f8c57a9cda93d2025-02-03T06:43:04ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-03-011410.1177/1550147718767376An improved geometric algorithm for indoor localizationJunhua YangYong LiWei ChengIndoor localization system using receive signal strength indicator from wireless access point has attracted lots of attention recently. Geometric method is one of the most widely used spatial graph algorithms to locate object in an indoor environment, but it does not achieve good results when it is applied to a limited amount of valid data, especially when using the trilateration method. On the other hand, localization based on fingerprint can achieve high accuracy but need to pay heavy manual labor for fingerprint database establishment. In this article, we propose a bilateral greed iteration localization method based on greedy algorithm in order to use all of the effective anchor points. Comparing to trilateration, fingerprint, and maximum-likelihood method, the bilateral greed iteration method improves the localization accuracy and reduces complexity of localization process. The method proposed, coupled with measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms trilateration and maximum-likelihood receive signal strength indicator–based indoor location methods without using any radio map information nor a complicated algorithm. Extensive experiment results in a Wi-Fi coverage office environment indicate that the proposed bilateral greed iteration method reduces the localization error, 63.55%, 9.93%, and 47.85%, compared to trilateration, fingerprint, and maximum-likelihood method, respectively.https://doi.org/10.1177/1550147718767376 |
| spellingShingle | Junhua Yang Yong Li Wei Cheng An improved geometric algorithm for indoor localization International Journal of Distributed Sensor Networks |
| title | An improved geometric algorithm for indoor localization |
| title_full | An improved geometric algorithm for indoor localization |
| title_fullStr | An improved geometric algorithm for indoor localization |
| title_full_unstemmed | An improved geometric algorithm for indoor localization |
| title_short | An improved geometric algorithm for indoor localization |
| title_sort | improved geometric algorithm for indoor localization |
| url | https://doi.org/10.1177/1550147718767376 |
| work_keys_str_mv | AT junhuayang animprovedgeometricalgorithmforindoorlocalization AT yongli animprovedgeometricalgorithmforindoorlocalization AT weicheng animprovedgeometricalgorithmforindoorlocalization AT junhuayang improvedgeometricalgorithmforindoorlocalization AT yongli improvedgeometricalgorithmforindoorlocalization AT weicheng improvedgeometricalgorithmforindoorlocalization |