Abstract
The traditional Kalman filter is developed based on the Gaussian assumption, and the filtering effect of the traditional Kalman filter will be significantly degraded when the noise is non-Gaussian distributed. The minimum error entropy (MEE) can resist the effect of non-Gaussian noise on the system. The estimation performance of the MEE-based filtering algorithm is impacted by the kernel bandwidth (KB). This work presents an Improved MEE unscented Kalman filter (IMEE-UKF) to solve the above problem. The simulation results indicate that the algorithm outperforms existing filtering methods in the case of non-Gaussian disturbances.
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References
Chen, S.Y.: Kalman filter for robot vision: a survey. IEEE Trans. Ind. Electron. 59(11), 4409–4420 (2012)
Chen, B., Dang, L., Gu, Y., Zheng, N., Principe, J.C.: Minimum Error Entropy Kalman Filter. IEEE Trans. Syst. Man Cybern.-Syst. 51(9), 5819–5829 (2021)
Kulikov, G.Y., Kulikova, M.V.: Hyperbolic-SVD-based square-root unscented Kalman filters in continuous-discrete target tracking scenarios. IEEE Trans. Autom. Control 67(1), 366–373 (2022)
Crassidis, J., Markley, F.: Unscented filtering for spacecraft attitude estimation. J. Guid. Control Dyn. 26(4), 536–542 (2003)
Acknowledgments
This work was assisted by the Area Research and Development Program of Guangdong Province (No.2020B0909020001).
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Chu, S., Qian, H., Yan, S. (2023). Improved Unscented Kalman Filter for Satellite Attitude Estimation. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_14
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DOI: https://doi.org/10.1007/978-981-19-6613-2_14
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