An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System

An ionospheric anomaly is the irregular change of the ionosphere. It may result in potential threats for the ground-based augmentation system (GBAS) supporting the high-level precision approach. To counter the hazardous anomalies caused by the steep gradient in ionospheric delays, customized monitor...

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Autores principales:	Zhen Gao, Kun Fang, Yanbo Zhu, Zhipeng Wang, Kai Guo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GNSS GBAS ionospheric gradient anomaly one class support vector machine Science Q
Acceso en línea:	https://doaj.org/article/646e6684e30e47bdbb43c0fb4670b121
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id	oai:doaj.org-article:646e6684e30e47bdbb43c0fb4670b121
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spelling	oai:doaj.org-article:646e6684e30e47bdbb43c0fb4670b1212021-11-11T18:53:56ZAn Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System10.3390/rs132143272072-4292https://doaj.org/article/646e6684e30e47bdbb43c0fb4670b1212021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4327https://doaj.org/toc/2072-4292An ionospheric anomaly is the irregular change of the ionosphere. It may result in potential threats for the ground-based augmentation system (GBAS) supporting the high-level precision approach. To counter the hazardous anomalies caused by the steep gradient in ionospheric delays, customized monitors are equipped in GBAS architectures. A major challenge is to rapidly detect the ionospheric gradient anomaly from environmental noise to meet the safety-critical requirements. A one-class support vector machine (OCSVM)-based monitor is developed to clearly detect ionospheric anomalies and to improve the robust detection speed. An offline-online framework based on the OCSVM is proposed to extract useful information related to anomalous characteristics in the presence of noise. To validate the effectiveness of the proposed framework, the influence of noise is fully considered and analyzed based on synthetic, semi-simulated, and real data from a typical ionospheric anomaly event. Synthetic results show that the OCSVM-based monitor can identify the anomaly that cannot be detected by other commonly-used monitors, such as the CCD-1OF, CCD-2OF and KLD-1OF. Semi-simulation results show that compared with other monitors, the newly proposed monitor can improve the average detection speed by more than 40% and decrease the minimum detectable gradient change rate to 0.002 m/s. Furthermore, in the real ionospheric anomaly event experiment, compared with other monitors, the OCSVM-based monitor can improve the detection speed by 16%. The result indicates that the proposed monitor has encouraging potential to ensure integrity of the GBAS.Zhen GaoKun FangYanbo ZhuZhipeng WangKai GuoMDPI AGarticleGNSSGBASionospheric gradient anomalyone class support vector machineScienceQENRemote Sensing, Vol 13, Iss 4327, p 4327 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GNSS GBAS ionospheric gradient anomaly one class support vector machine Science Q
spellingShingle	GNSS GBAS ionospheric gradient anomaly one class support vector machine Science Q Zhen Gao Kun Fang Yanbo Zhu Zhipeng Wang Kai Guo An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
description	An ionospheric anomaly is the irregular change of the ionosphere. It may result in potential threats for the ground-based augmentation system (GBAS) supporting the high-level precision approach. To counter the hazardous anomalies caused by the steep gradient in ionospheric delays, customized monitors are equipped in GBAS architectures. A major challenge is to rapidly detect the ionospheric gradient anomaly from environmental noise to meet the safety-critical requirements. A one-class support vector machine (OCSVM)-based monitor is developed to clearly detect ionospheric anomalies and to improve the robust detection speed. An offline-online framework based on the OCSVM is proposed to extract useful information related to anomalous characteristics in the presence of noise. To validate the effectiveness of the proposed framework, the influence of noise is fully considered and analyzed based on synthetic, semi-simulated, and real data from a typical ionospheric anomaly event. Synthetic results show that the OCSVM-based monitor can identify the anomaly that cannot be detected by other commonly-used monitors, such as the CCD-1OF, CCD-2OF and KLD-1OF. Semi-simulation results show that compared with other monitors, the newly proposed monitor can improve the average detection speed by more than 40% and decrease the minimum detectable gradient change rate to 0.002 m/s. Furthermore, in the real ionospheric anomaly event experiment, compared with other monitors, the OCSVM-based monitor can improve the detection speed by 16%. The result indicates that the proposed monitor has encouraging potential to ensure integrity of the GBAS.
format	article
author	Zhen Gao Kun Fang Yanbo Zhu Zhipeng Wang Kai Guo
author_facet	Zhen Gao Kun Fang Yanbo Zhu Zhipeng Wang Kai Guo
author_sort	Zhen Gao
title	An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
title_short	An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
title_full	An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
title_fullStr	An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
title_full_unstemmed	An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
title_sort	ionospheric anomaly monitor based on the one class support vector algorithm for the ground-based augmentation system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/646e6684e30e47bdbb43c0fb4670b121
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An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System

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