Adaptive Robust Minimum Error Entropy Unscented Kalman Filter for Satellite Attitude Estimation
Publication: Journal of Aerospace Engineering
Volume 35, Issue 5
Abstract
In recent years, the Kalman filter based on the minimum error entropy (MEE) criterion has been proposed, which outperforms the traditional Kalman filter in the presence of non-Gaussian noise. In practical applications, the estimated performance of the MEE unscented Kalman filter (MEE-UKF) algorithm is influenced by the kernel bandwidth (KB). In addition, it may be unstable in numerical computation. This paper proposes an adaptive robust MEE unscented Kalman filter (AMEE-UKF) to address the problem of instability in numerical computation. In addition, by setting an adaptive factor to optimize the MEE-UKF, an appropriate value of the KB can be obtained adaptively. The high accuracy and robustness of the AMEE-UKF were demonstrated by the simulation experiments.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was cosupported by the Area Research and Development Program of Guangdong Province (No. 2020B0909020001) and the National Natural Science Foundation of China (No. 61573113).
References
Bo, X., A. A. Razzaqi, and L. Yalong. 2019. “Cooperative localisation of AUVs based on Huber-based robust algorithm and adaptive noise estimation.” J. Navig. 72 (4): 875–893. https://doi.org/10.1017/S0373463319000018.
Chang, L., B. Hu, G. Chang, and A. Li. 2012. “Huber-based novel robust unscented Kalman filter.” IET Sci. Meas. Technol. 6 (6): 502–509. https://doi.org/10.1049/iet-smt.2011.0169.
Chen, B., L. Dang, Y. Gu, N. Zheng, and J. C. Principe. 2021a. “Minimum error entropy Kalman filter.” IEEE Trans. Syst. Man Cybern.: Syst. 51 (9): 5819–5829. https://doi.org/10.1109/TSMC.2019.2957269.
Chen, B., and J. C. Principe. 2012. “Maximum correntropy estimation is a smoothed MAP estimation.” IEEE Signal Process Lett. 19 (8): 491–494. https://doi.org/10.1109/LSP.2012.2204435.
Chen, B., P. Zhu, and J. C. Principe. 2012. “Survival information potential: A new criterion for adaptive system training.” IEEE Trans. Signal Process. 60 (3): 1184–1194. https://doi.org/10.1109/TSP.2011.2178406.
Chen, S. Y. 2012. “Kalman filter for robot vision: A survey.” IEEE Trans. Ind. Electron. 59 (11): 4409–4420. https://doi.org/10.1109/TIE.2011.2162714.
Chen, X., Z. Ma, and P. Yang. 2021b. “Integrated modeling of motion decoupling and flexure deformation of carrier in transfer alignment.” Mech. Syst. Sig. Process. 159 (Oct): 107690. https://doi.org/10.1016/j.ymssp.2021.107690.
Crassidis, J., and F. Markley. 2003. “Unscented filtering for spacecraft attitude estimation.” J. Guidance Control Dyn. 26 (4): 536–542. https://doi.org/10.2514/2.5102.
Crassidis, J. L. 2006. “Sigma-point Kalman filtering for integrated GPS and inertial navigation.” IEEE Trans. Aerosp. Electron. Syst. 42 (2): 750–756. https://doi.org/10.1109/TAES.2006.1642588.
Crassidis, J. L., F. L. Markley, and Y. Cheng. 2007. “Survey of nonlinear attitude estimation methods.” J. Guidance Control Dyn. 30 (1): 12–28. https://doi.org/10.2514/1.22452.
Dang, L., B. Chen, S. Wang, W. Ma, and P. Ren. 2020. “Robust power system state estimation with minimum error entropy unscented Kalman filter.” IEEE Trans. Instrum. Meas. 69 (11): 8797–8808. https://doi.org/10.1109/TIM.2020.2999757.
Dini, D. H., D. P. Mandic, and S. J. Julier. 2011. “A widely linear complex unscented Kalman filter.” IEEE Signal Process Lett. 18 (11): 623–626. https://doi.org/10.1109/LSP.2011.2166259.
Farrenkopf, R. 1978. “Analytic steady-state accuracy solutions for two common spacecraft attitude estimators.” J. Guidance Control 1 (4): 282–284. https://doi.org/10.2514/3.55779.
Huang, Y., Y. Zhang, Z. Wu, N. Li, and J. Chambers. 2018a. “A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices.” IEEE Trans. Autom. Control 63 (2): 594–601. https://doi.org/10.1109/TAC.2017.2730480.
Huang, Y., Y. Zhang, B. Xu, Z. Wu, and J. A. Chambers. 2018b. “A new adaptive extended Kalman filter for cooperative localization.” IEEE Trans. Aerosp. Electron. Syst. 54 (1): 353–368. https://doi.org/10.1109/TAES.2017.2756763.
Jahanchahi, C., and D. P. Mandic. 2014. “A class of quaternion Kalman filters.” IEEE Trans. Neural Networks Learn. Syst. 25 (3): 533–544. https://doi.org/10.1109/TNNLS.2013.2277540.
Jiao, Y., H. Zhou, J. Wang, and J. Li. 2012. “Linearization error’s measure and its influence on the accuracy of MEKF based attitude determination method.” Aerosp. Sci. Technol. 16 (1): 61–69. https://doi.org/10.1016/j.ast.2011.05.004.
Karlgaard, C. D., and H. Schaub. 2011. “Adaptive nonlinear Huber-based navigation for rendezvous in elliptical orbit.” J. Guidance Control Dyn. 34 (2): 388–402. https://doi.org/10.2514/1.51939.
Li, W., H. Leung, and Y. Zhou. 2004. “Space-time registration of radar and ESM using unscented Kalman filter.” IEEE Trans. Aerosp. Electron. Syst. 40 (3): 824–836. https://doi.org/10.1109/TAES.2004.1337457.
Li, W., G. Wei, D. Ding, Y. Liu, and F. E. Alsaadi. 2018. “A new look at boundedness of error covariance of Kalman filtering.” IEEE Trans. Syst. Man Cybern.: Syst. 48 (2): 309–314. https://doi.org/10.1109/TSMC.2016.2598845.
Liu, D., X. Chen, Y. Xu, X. Liu, and C. Shi. 2019. “Maximum correntropy generalized high-degree cubature Kalman filter with application to the attitude determination system of missile.” Aerosp. Sci. Technol. 95 (Dec): 105441. https://doi.org/10.1016/j.ast.2019.105441.
Liu, W., P. P. Pokharel, and J. C. Principe. 2007. “Correntropy: Properties and applications in non-Gaussian signal processing.” IEEE Trans. Signal Process. 55 (11): 5286–5298. https://doi.org/10.1109/TSP.2007.896065.
Liu, X., B. Chen, B. Xu, Z. Wu, and P. Honeine. 2017. “Maximum correntropy unscented filter.” Int. J. Syst. Sci. 48 (8): 1607–1615. https://doi.org/10.1080/00207721.2016.1277407.
Qi, J., K. Sun, J. Wang, and H. Liu. 2018. “Dynamic state estimation for multi-machine power system by unscented Kalman filter with enhanced numerical stability.” IEEE Trans. Smart Grid 9 (2): 1184–1196. https://doi.org/10.1109/TSG.2016.2580584.
Qiu, Z., and H. Qian. 2018. “Adaptive genetic particle filter and its application to attitude estimation system.” Digital Signal Process. 81 (Oct): 163–172. https://doi.org/10.1016/j.dsp.2018.06.015.
Qiu, Z., H. Qian, and G. Wang. 2018. “Adaptive robust cubature Kalman filtering for satellite attitude estimation.” Chin. J. Aeronaut. 31 (4): 806–819. https://doi.org/10.1016/j.cja.2018.01.023.
Santamaria, I., D. Erdogmus, and J. Principe. 2002. “Entropy minimization for supervised digital communications channel equalization.” IEEE Trans. Signal Process. 50 (5): 1184–1192. https://doi.org/10.1109/78.995074.
Shuster, M. D. 1989. “Maximum likelihood estimation of spacecraft attitude.” J. Astronaut. Sci. 37 (1): 79–88. https://doi.org/10.1080/00949657808810232.
Soken, H. E., and C. Hajiyev. 2010. “Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults.” ISA Trans. 49 (3): 249–256. https://doi.org/10.1016/j.isatra.2010.04.001.
Talebi, S. P., S. Kanna, and D. P. Mandic. 2016. “A distributed quaternion Kalman filter with applications to smart grid and target tracking.” IEEE Trans. Signal Inf. Process. Networks 2 (4): 477–488. https://doi.org/10.1109/TSIPN.2016.2618321.
Wang, C., J. Zhang, and J. Mu. 2012. “Maximum likelihood-based iterated divided difference filter for nonlinear systems from discrete noisy measurements.” Sensors 12 (7): 8912–8929. https://doi.org/10.3390/s120708912.
Wang, G., B. Chen, X. Yang, B. Peng, and Z. Feng. 2021. “Numerically stable minimum error entropy Kalman filter.” Signal Process. 181: 107914. https://doi.org/10.1016/j.sigpro.2020.107914.
Wang, G., N. Li, and Y. Zhang. 2017. “Maximum correntropy unscented Kalman and information filters for non-Gaussian measurement noise.” J. Franklin Inst. 354 (18): 8659–8677. https://doi.org/10.1016/j.jfranklin.2017.10.023.
Wang, X., N. Cui, and J. Guo. 2010. “Huber-based unscented filtering and its application to vision-based relative navigation.” IET Radar Sonar Navig. 4 (1): 134–141. https://doi.org/10.1049/iet-rsn.2009.0170.
Xiang, M., Y. Xia, and D. P. Mandic. 2019. “Complementary cost functions for complex and quaternion widely linear estimation.” IEEE Signal Process Lett. 26 (9): 1344–1348. https://doi.org/10.1109/LSP.2019.2931426.
Xiong, K., L. D. Liu, and H. Y. Zhang. 2009. “Modified unscented Kalman filtering and its application in autonomous satellite navigation.” Aerosp. Sci. Technol. 13 (4–5): 238–246. https://doi.org/10.1016/j.ast.2009.04.001.
Yang, Y. 2012. “Spacecraft attitude determination and control: Quaternion based method.” Ann. Rev. Control 36 (2): 198–219. https://doi.org/10.1016/j.arcontrol.2012.09.003.
Zhang, Y., B. Chen, X. Liu, Z. Yuan, and J. C. Principe. 2015. “Convergence of a fixed-point minimum error entropy algorithm.” Entropy (Basel) 17 (12): 5549–5560. https://doi.org/10.3390/e17085549.
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Published In
Journal of Aerospace Engineering
Volume 35 • Issue 5 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 10, 2021
Accepted: Apr 4, 2022
Published online: May 26, 2022
Published in print: Sep 1, 2022
Discussion open until: Oct 26, 2022
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