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Remote Sensing
Volume 14
Issue 5
10.3390/rs14051111
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Article

An Error Overbounding Method Based on a Gaussian Mixture Model with Uncertainty Estimation for a Dual-Frequency Ground-Based Augmentation System

by
Zhen Gao
1,
Kun Fang
2,
Zhipeng Wang
1,*,
Kai Guo
3 and
Yuan Liu
1
1
National Key Laboratory of CNS/ATM, School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
2
Research Institute for Frontier Science, Beihang University, Beijing 100191, China
3
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(5), 1111; https://doi.org/10.3390/rs14051111
Submission received: 22 January 2022 / Revised: 22 February 2022 / Accepted: 22 February 2022 / Published: 24 February 2022
(This article belongs to the Special Issue Remote Sensing in Navigation: State-of-the-Art)
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Abstract

To ensure the integrity of a ground-based augmentation system (GBAS), an ionosphere-free (Ifree) filtering algorithm with dual-frequency measurements is employed to make the GBAS free of the first-order ionospheric influence. However, the Ifree algorithm outputs the errors of two frequencies. The protection level obtained via the traditional Gaussian overbound is overconservative. This conservatism may cause false alarms and diminish availability. An overbounding framework based on a Gaussian mixture model (GMM) is proposed to handle samples drawn from Ifree-based GBAS range errors. The GMM is employed to model the single-frequency errors that concern the uncertainty estimation. A Monte Carlo simulation is performed to determine the accuracy of the estimated GMM confidence level obtained by using the general estimation approach. Then, the final GMM used to overbound the Ifree error distribution is analyzed. Based on the convolution invariance property, vertical protection levels in the position domain are explicitly derived without introducing complex numerical calculations. A performance evaluation based on a real-world road test shows that the Ifree-based vertical protection levels are tightened with a small computational cost.
Keywords: GBAS; overbound; Gaussian mixture model (GMM); dual-frequency GBAS; overbound; Gaussian mixture model (GMM); dual-frequency

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MDPI and ACS Style

Gao, Z.; Fang, K.; Wang, Z.; Guo, K.; Liu, Y. An Error Overbounding Method Based on a Gaussian Mixture Model with Uncertainty Estimation for a Dual-Frequency Ground-Based Augmentation System. Remote Sens. 2022, 14, 1111. https://doi.org/10.3390/rs14051111

AMA Style

Gao Z, Fang K, Wang Z, Guo K, Liu Y. An Error Overbounding Method Based on a Gaussian Mixture Model with Uncertainty Estimation for a Dual-Frequency Ground-Based Augmentation System. Remote Sensing. 2022; 14(5):1111. https://doi.org/10.3390/rs14051111

Chicago/Turabian Style

Gao, Zhen, Kun Fang, Zhipeng Wang, Kai Guo, and Yuan Liu. 2022. "An Error Overbounding Method Based on a Gaussian Mixture Model with Uncertainty Estimation for a Dual-Frequency Ground-Based Augmentation System" Remote Sensing 14, no. 5: 1111. https://doi.org/10.3390/rs14051111

APA Style

Gao, Z., Fang, K., Wang, Z., Guo, K., & Liu, Y. (2022). An Error Overbounding Method Based on a Gaussian Mixture Model with Uncertainty Estimation for a Dual-Frequency Ground-Based Augmentation System. Remote Sensing, 14(5), 1111. https://doi.org/10.3390/rs14051111

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