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http://dx.doi.org/10.46670/JSST.2020.29.6.440

Effects of Covariance Modeling on Estimation Accuracy in an IMU-based Attitude Estimation Kalman Filter  

Choi, Ji Seok (Mechanical Engineering, Hankyong National University)
Lee, Jung Keun (School of ICT, Robotics and Mechanical Engineering, Hankyong National University)
Publication Information
Journal of Sensor Science and Technology / v.29, no.6, 2020 , pp. 440-446 More about this Journal
Abstract
A well-known difficulty in attitude estimation based on inertial measurement unit (IMU) signals is the occurrence of external acceleration under dynamic motion conditions, as the acceleration significantly degrades the estimation accuracy. Lee et al. (2012) designed a Kalman filter (KF) that could effectively deal with the acceleration issue. Ahmed and Tahir (2017) modified this method by adjusting the acceleration-related covariance matrix because they considered covariance modeling as a pivotal factor in the estimation accuracy. This study investigates the effects of covariance modeling on estimation accuracy in an IMU-based attitude estimation KF. The method proposed by Ahmed and Tahir can be divided into two: one uses the covariance including only diagonal components and the other uses the covariance including both diagonal and off-diagonal components. This paper compares these three methods with respect to the motion condition and the window size, which is required for the methods by Ahmed and Tahir. Experimental results showed that the method proposed by Lee et al. performed the best among the three methods under relatively slow motion conditions, whereas the modified method using the diagonal covariance with a high window size performed the best under relatively fast motion conditions.
Keywords
Covariance; Attitude estimation; IMU; Kalman filter; External acceleration; Human motion tracking;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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