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...
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doaj-37a4e84be48f44d49b9492cdb217efad2021-08-06T15:31:18ZengMDPI AGSensors1424-82202021-07-01215055505510.3390/s21155055An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad AlgorithmZeyang Wen0Gongliu Yang1Qingzhong Cai2School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaIn 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.https://www.mdpi.com/1424-8220/21/15/5055inertial measurement unit (IMU) calibrationstrapdown inertial navigation system (SINS)Kalman filtergradient descent |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zeyang Wen Gongliu Yang Qingzhong Cai |
spellingShingle |
Zeyang Wen Gongliu Yang Qingzhong Cai An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm Sensors inertial measurement unit (IMU) calibration strapdown inertial navigation system (SINS) Kalman filter gradient descent |
author_facet |
Zeyang Wen Gongliu Yang Qingzhong Cai |
author_sort |
Zeyang Wen |
title |
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_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_sort |
improved calibration method for the imu biases utilizing kf-based adagrad algorithm |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-07-01 |
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. |
topic |
inertial measurement unit (IMU) calibration strapdown inertial navigation system (SINS) Kalman filter gradient descent |
url |
https://www.mdpi.com/1424-8220/21/15/5055 |
work_keys_str_mv |
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_version_ |
1721217570650980352 |