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A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning
The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349536/ https://www.ncbi.nlm.nih.gov/pubmed/32575892 http://dx.doi.org/10.3390/s20123514 |
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author | He, Chengyang Tang, Chao Yu, Chengpu |
author_facet | He, Chengyang Tang, Chao Yu, Chengpu |
author_sort | He, Chengyang |
collection | PubMed |
description | The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively. |
format | Online Article Text |
id | pubmed-7349536 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73495362020-07-14 A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning He, Chengyang Tang, Chao Yu, Chengpu Sensors (Basel) Article The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively. MDPI 2020-06-21 /pmc/articles/PMC7349536/ /pubmed/32575892 http://dx.doi.org/10.3390/s20123514 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article He, Chengyang Tang, Chao Yu, Chengpu A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning |
title | A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning |
title_full | A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning |
title_fullStr | A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning |
title_full_unstemmed | A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning |
title_short | A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning |
title_sort | federated derivative cubature kalman filter for imu-uwb indoor positioning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349536/ https://www.ncbi.nlm.nih.gov/pubmed/32575892 http://dx.doi.org/10.3390/s20123514 |
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