Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning
An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/10/3/319 |
| _version_ | 1797609428833271808 |
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| author | Kaiyao Wang Yi He Xinpeng Huang Zhiyong Hong |
| author_facet | Kaiyao Wang Yi He Xinpeng Huang Zhiyong Hong |
| author_sort | Kaiyao Wang |
| collection | DOAJ |
| description | An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the CSI. The direct path distance estimated by the CSI is utilized to construct a position set consisting of multiple positions and a corresponding distance database containing multiple distance vectors. The error parameters of the weighted combinations of different distance vectors are calculated iteratively to evaluate the impact of different K-values and weights on the positioning accuracy. The proposed algorithm can achieve high-precision trilateration positioning by adaptively selecting the K-value and weight. A typical 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to simulate the positioning performance. The simulation results reveal that the mean error of the CSI-based AWKNN algorithm achieves 1.84 cm, with a root mean square error (RMSE) of 2.13 cm. Compared with the CSI-based least squares (LS) method, the CSI-based nonlinear LS method, and the CSI-based WKNN method, the average error of this method is decreased by 29%, 16%, and 17%, whereas the RMSE is reduced by 35%, 14%, and 19%. |
| first_indexed | 2024-03-11T06:01:14Z |
| format | Article |
| id | doaj.art-6dda62f9ab484d59869c203cc2cd4017 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-11T06:01:14Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-6dda62f9ab484d59869c203cc2cd40172023-11-17T13:19:21ZengMDPI AGPhotonics2304-67322023-03-0110331910.3390/photonics10030319Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light PositioningKaiyao Wang0Yi He1Xinpeng Huang2Zhiyong Hong3Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, ChinaFaculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, ChinaFaculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, ChinaFaculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, ChinaAn adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the CSI. The direct path distance estimated by the CSI is utilized to construct a position set consisting of multiple positions and a corresponding distance database containing multiple distance vectors. The error parameters of the weighted combinations of different distance vectors are calculated iteratively to evaluate the impact of different K-values and weights on the positioning accuracy. The proposed algorithm can achieve high-precision trilateration positioning by adaptively selecting the K-value and weight. A typical 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to simulate the positioning performance. The simulation results reveal that the mean error of the CSI-based AWKNN algorithm achieves 1.84 cm, with a root mean square error (RMSE) of 2.13 cm. Compared with the CSI-based least squares (LS) method, the CSI-based nonlinear LS method, and the CSI-based WKNN method, the average error of this method is decreased by 29%, 16%, and 17%, whereas the RMSE is reduced by 35%, 14%, and 19%.https://www.mdpi.com/2304-6732/10/3/319visible light positioningtrilateration positioningadaptive weighted K-nearest neighborchannel state information |
| spellingShingle | Kaiyao Wang Yi He Xinpeng Huang Zhiyong Hong Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning Photonics visible light positioning trilateration positioning adaptive weighted K-nearest neighbor channel state information |
| title | Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning |
| title_full | Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning |
| title_fullStr | Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning |
| title_full_unstemmed | Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning |
| title_short | Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning |
| title_sort | adaptive weighted k nearest neighbor trilateration algorithm for visible light positioning |
| topic | visible light positioning trilateration positioning adaptive weighted K-nearest neighbor channel state information |
| url | https://www.mdpi.com/2304-6732/10/3/319 |
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