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Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

Dah-Jing Jwo*, Jui-Tao Lee

Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung, 202301, Taiwan

* Corresponding Author: Dah-Jing Jwo. Email: email

Computers, Materials & Continua 2021, 68(1), 857-876. https://doi.org/10.32604/cmc.2021.016894

Abstract

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

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APA Style
Jwo, D., Lee, J. (2021). Kernel entropy based extended kalman filter for GPS navigation processing. Computers, Materials & Continua, 68(1), 857-876. https://doi.org/10.32604/cmc.2021.016894
Vancouver Style
Jwo D, Lee J. Kernel entropy based extended kalman filter for GPS navigation processing. Comput Mater Contin. 2021;68(1):857-876 https://doi.org/10.32604/cmc.2021.016894
IEEE Style
D. Jwo and J. Lee, “Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing,” Comput. Mater. Contin., vol. 68, no. 1, pp. 857-876, 2021. https://doi.org/10.32604/cmc.2021.016894



cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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