Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Effects of Covariance Modeling on Estimation Accuracy in an IMU-based Attitude Estimation Kalman Filter

IMU 기반 자세 추정 칼만필터에서 공분산 모델링이 추정 정확도에 미치는 영향

  • Choi, Ji Seok (Mechanical Engineering, Hankyong National University) ;
  • Lee, Jung Keun (School of ICT, Robotics and Mechanical Engineering, Hankyong National University)
  • 최지석 (한경대학교 기계공학과) ;
  • 이정근 (한경대학교 ICT로봇기계공학부)
  • Received : 2020.11.18
  • Accepted : 2020.11.29
  • Published : 2020.11.30
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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.

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References

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