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An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost
Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outlier...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142240/ https://www.ncbi.nlm.nih.gov/pubmed/37112293 http://dx.doi.org/10.3390/s23083952 |
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author | Lu, Haizhao Zhang, Lieping Chen, Hongyuan Zhang, Shenglan Wang, Shoufeng Peng, Huihao Zou, Jianchu |
author_facet | Lu, Haizhao Zhang, Lieping Chen, Hongyuan Zhang, Shenglan Wang, Shoufeng Peng, Huihao Zou, Jianchu |
author_sort | Lu, Haizhao |
collection | PubMed |
description | Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26–45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance. |
format | Online Article Text |
id | pubmed-10142240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101422402023-04-29 An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost Lu, Haizhao Zhang, Lieping Chen, Hongyuan Zhang, Shenglan Wang, Shoufeng Peng, Huihao Zou, Jianchu Sensors (Basel) Article Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26–45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance. MDPI 2023-04-13 /pmc/articles/PMC10142240/ /pubmed/37112293 http://dx.doi.org/10.3390/s23083952 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lu, Haizhao Zhang, Lieping Chen, Hongyuan Zhang, Shenglan Wang, Shoufeng Peng, Huihao Zou, Jianchu An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost |
title | An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost |
title_full | An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost |
title_fullStr | An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost |
title_full_unstemmed | An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost |
title_short | An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost |
title_sort | indoor fingerprint positioning algorithm based on wknn and improved xgboost |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142240/ https://www.ncbi.nlm.nih.gov/pubmed/37112293 http://dx.doi.org/10.3390/s23083952 |
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