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An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm
In the field of high accuracy strapdown inertial navigation system (SINS), the inertial measurement unit (IMU) biases can severely affect the navigation accuracy. Traditionally we use Kalman filter (KF) to estimate those biases. However, KF is an unbiased estimation method based on the assumption of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347962/ https://www.ncbi.nlm.nih.gov/pubmed/34372290 http://dx.doi.org/10.3390/s21155055 |
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author | Wen, Zeyang Yang, Gongliu Cai, Qingzhong |
author_facet | Wen, Zeyang Yang, Gongliu Cai, Qingzhong |
author_sort | Wen, Zeyang |
collection | PubMed |
description | In the field of high accuracy strapdown inertial navigation system (SINS), the inertial measurement unit (IMU) biases can severely affect the navigation accuracy. Traditionally we use Kalman filter (KF) to estimate those biases. However, KF is an unbiased estimation method based on the assumption of Gaussian white noise (GWN) while IMU sensors noise is irregular. Kalman filtering will no longer be accurate when the sensor’s noise is irregular. In order to obtain the optimal solution of the IMU biases, this paper proposes a novel method for the calibration of IMU biases utilizing the KF-based AdaGrad algorithm to solve this problem. Three improvements were made as the following: (1) The adaptive subgradient method (AdaGrad) is proposed to overcome the difficulty of setting step size. (2) A KF-based AdaGrad numerical function is derived and (3) a KF-based AdaGrad calibration algorithm is proposed in this paper. Experimental results show that the method proposed in this paper can effectively improve the accuracy of IMU biases in both static tests and car-mounted field tests. |
format | Online Article Text |
id | pubmed-8347962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83479622021-08-08 An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm Wen, Zeyang Yang, Gongliu Cai, Qingzhong Sensors (Basel) Article In the field of high accuracy strapdown inertial navigation system (SINS), the inertial measurement unit (IMU) biases can severely affect the navigation accuracy. Traditionally we use Kalman filter (KF) to estimate those biases. However, KF is an unbiased estimation method based on the assumption of Gaussian white noise (GWN) while IMU sensors noise is irregular. Kalman filtering will no longer be accurate when the sensor’s noise is irregular. In order to obtain the optimal solution of the IMU biases, this paper proposes a novel method for the calibration of IMU biases utilizing the KF-based AdaGrad algorithm to solve this problem. Three improvements were made as the following: (1) The adaptive subgradient method (AdaGrad) is proposed to overcome the difficulty of setting step size. (2) A KF-based AdaGrad numerical function is derived and (3) a KF-based AdaGrad calibration algorithm is proposed in this paper. Experimental results show that the method proposed in this paper can effectively improve the accuracy of IMU biases in both static tests and car-mounted field tests. MDPI 2021-07-26 /pmc/articles/PMC8347962/ /pubmed/34372290 http://dx.doi.org/10.3390/s21155055 Text en © 2021 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 Wen, Zeyang Yang, Gongliu Cai, Qingzhong An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm |
title | An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm |
title_full | An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm |
title_fullStr | An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm |
title_full_unstemmed | An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm |
title_short | An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm |
title_sort | improved calibration method for the imu biases utilizing kf-based adagrad algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347962/ https://www.ncbi.nlm.nih.gov/pubmed/34372290 http://dx.doi.org/10.3390/s21155055 |
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