Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system
The indoor localisation based on indoor magnetic field (MF) has drawn much research attention since they have a range of applications field in science and industry. The position estimation is generally based on the Euclidean distance (ED) between compared data points. Commonly, the state-of-the-art...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/97414/ http://psasir.upm.edu.my/id/eprint/97414/1/ABSTRACT.pdf |
| _version_ | 1848862593074069504 |
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| author | Bundak, Caceja Elyca Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis Osman, Nurul Huda |
| author_facet | Bundak, Caceja Elyca Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis Osman, Nurul Huda |
| author_sort | Bundak, Caceja Elyca |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The indoor localisation based on indoor magnetic field (MF) has drawn much research attention since they have a range of applications field in science and industry. The position estimation is generally based on the Euclidean distance (ED) between compared data points. Commonly, the state-of-the-art k-nearest neighbour (KNN) algorithm is used to estimate the test point (TP) position by considering the average location of the closest estimated K reference points (RPs). However, the problem of using the KNN algorithm is the fixed K value does not guarantee accurate estimation at every position. In this study, we first optimise the MF RPs database using the clustering method. Each trained RP and other nearby RPs are clustered together at a certain distance. Then, we create a rank cluster algorithm where we match the top 10 ranks RPs with the nearest Euclidean distance to the TP with the RPs cluster. For the proposed fuzzy algorithm, a condition is applied to choose whether the triangle area or average Euclidean algorithm is used to find the final estimated position. Experiments show a localisation accuracy of 5.88 m, which is better than KNN with an improvement of 31 %. |
| first_indexed | 2025-11-15T13:19:29Z |
| format | Article |
| id | upm-97414 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:19:29Z |
| publishDate | 2021 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-974142022-08-26T08:49:09Z http://psasir.upm.edu.my/id/eprint/97414/ Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system Bundak, Caceja Elyca Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis Osman, Nurul Huda The indoor localisation based on indoor magnetic field (MF) has drawn much research attention since they have a range of applications field in science and industry. The position estimation is generally based on the Euclidean distance (ED) between compared data points. Commonly, the state-of-the-art k-nearest neighbour (KNN) algorithm is used to estimate the test point (TP) position by considering the average location of the closest estimated K reference points (RPs). However, the problem of using the KNN algorithm is the fixed K value does not guarantee accurate estimation at every position. In this study, we first optimise the MF RPs database using the clustering method. Each trained RP and other nearby RPs are clustered together at a certain distance. Then, we create a rank cluster algorithm where we match the top 10 ranks RPs with the nearest Euclidean distance to the TP with the RPs cluster. For the proposed fuzzy algorithm, a condition is applied to choose whether the triangle area or average Euclidean algorithm is used to find the final estimated position. Experiments show a localisation accuracy of 5.88 m, which is better than KNN with an improvement of 31 %. Elsevier 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/97414/1/ABSTRACT.pdf Bundak, Caceja Elyca and Abd Rahman, Mohd Amiruddin and Abdul Karim, Muhammad Khalis and Osman, Nurul Huda (2021) Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system. Alexandria Engineering Journal, 61 (5). pp. 3645-3655. ISSN 2090-2670 https://www.sciencedirect.com/science/article/pii/S1110016821005883 10.1016/j.aej.2021.08.073 |
| spellingShingle | Bundak, Caceja Elyca Abd Rahman, Mohd Amiruddin Abdul Karim, Muhammad Khalis Osman, Nurul Huda Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system |
| title | Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system |
| title_full | Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system |
| title_fullStr | Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system |
| title_full_unstemmed | Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system |
| title_short | Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system |
| title_sort | fuzzy rank cluster top k euclidean distance and triangle based algorithm for magnetic field indoor positioning system |
| url | http://psasir.upm.edu.my/id/eprint/97414/ http://psasir.upm.edu.my/id/eprint/97414/ http://psasir.upm.edu.my/id/eprint/97414/ http://psasir.upm.edu.my/id/eprint/97414/1/ABSTRACT.pdf |