Sensors, Vol. 21, Pages 5055: An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm

Sensors, Vol. 21, Pages 5055: An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm
Sensors doi: 10.3390/s21155055
Authors:
Zeyang Wen
Gongliu Yang
Qingzhong Cai

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.

Source: Sensors - July 26, 2021 Category: Biotechnology Authors: Zeyang Wen Gongliu Yang Qingzhong Cai Tags: Article Source Type: research