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An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning
The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimati...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371230/ https://www.ncbi.nlm.nih.gov/pubmed/35957248 http://dx.doi.org/10.3390/s22155689 |
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author | Yang, Jingmin Deng, Shanghui Lin, Minmin Xu, Li |
author_facet | Yang, Jingmin Deng, Shanghui Lin, Minmin Xu, Li |
author_sort | Yang, Jingmin |
collection | PubMed |
description | The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. |
format | Online Article Text |
id | pubmed-9371230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93712302022-08-12 An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning Yang, Jingmin Deng, Shanghui Lin, Minmin Xu, Li Sensors (Basel) Article The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. MDPI 2022-07-29 /pmc/articles/PMC9371230/ /pubmed/35957248 http://dx.doi.org/10.3390/s22155689 Text en © 2022 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 Yang, Jingmin Deng, Shanghui Lin, Minmin Xu, Li An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning |
title | An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning |
title_full | An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning |
title_fullStr | An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning |
title_full_unstemmed | An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning |
title_short | An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning |
title_sort | adaptive calibration algorithm based on rssi and ldplm for indoor ranging and positioning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371230/ https://www.ncbi.nlm.nih.gov/pubmed/35957248 http://dx.doi.org/10.3390/s22155689 |
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