An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System Article Swipe
Zhen Gao
,
Kun Fang
,
Yanbo Zhu
,
Zhipeng Wang
,
Kai Guo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13214327
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13214327
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.
Related Topics
Concepts
Anomaly detection
Anomaly (physics)
Computer science
Ionosphere
GNSS augmentation
Event (particle physics)
Noise (video)
Real-time computing
Remote sensing
Geology
Artificial intelligence
Global Positioning System
Geophysics
GNSS applications
Telecommunications
Physics
Condensed matter physics
Quantum mechanics
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13214327
- https://www.mdpi.com/2072-4292/13/21/4327/pdf?version=1635407976
- OA Status
- gold
- Cited By
- 4
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3208656062
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3208656062Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs13214327Digital Object Identifier
- Title
-
An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation SystemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-28Full publication date if available
- Authors
-
Zhen Gao, Kun Fang, Yanbo Zhu, Zhipeng Wang, Kai GuoList of authors in order
- Landing page
-
https://doi.org/10.3390/rs13214327Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/13/21/4327/pdf?version=1635407976Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/13/21/4327/pdf?version=1635407976Direct OA link when available
- Concepts
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Anomaly detection, Anomaly (physics), Computer science, Ionosphere, GNSS augmentation, Event (particle physics), Noise (video), Real-time computing, Remote sensing, Geology, Artificial intelligence, Global Positioning System, Geophysics, GNSS applications, Telecommunications, Physics, Condensed matter physics, Quantum mechanics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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