Abstract
To improve the computational efficiency and dynamic performance of low cost Inertial Measurement Unit (IMU)/magnetometer integrated Attitude and Heading Reference Systems (AHRS), this paper has proposed an effective Adaptive Kalman Filter (AKF) with linear models; the filter gain is adaptively tuned according to the dynamic scale sensed by accelerometers. This proposed approach does not need to model the system angular motions, avoids the non-linear problem which is inherent in the existing methods, and considers the impact of the dynamic acceleration on the filter. The experimental results with real data have demonstrated that the proposed algorithm can maintain an accurate estimation of orientation, even under various dynamic operating conditions.
Publisher
Cambridge University Press (CUP)
Subject
Ocean Engineering,Oceanography
Reference24 articles.
1. Wang J. , Gopaul N. and Guo J. (2010). Adaptive Kalman filtering based on posteriori variance-covariance components estimation, Proceedings of the CPGPS 2010 Navigation and Location Services: Emerging Industry and International Exchanges, Shanghai, China.
2. On the Parametrization of the Three-Dimensional Rotation Group
3. Qin W. , Yuan W. , Chang H. , Xue L. and Yuan G. (2009). Fuzzy adaptive extended Kalman filter for miniature attitude and heading reference system. Proceeding of the 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Shenzhen, China.
4. Lam Q. M. , Stamatakos N. , Woodruff C. and Ashtom S. (2003). Gyro modeling and estimation of its random noise sources. Proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibition, Austin, Texas.
5. Caruso M. J. (2000). Applications of magnetic sensors for low cost compass systems. Proceedings of the IEEE Position Location and Navigation Symposium, San Diego, CA.
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