An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
复制标题
基于一类支持向量算法的地基增强系统电离层异常监测
DOI：
10.3390/rs13214327
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发表时间：
2021-10
期刊：
影响因子：
5
通讯作者：
Kai Guo
中科院分区：
文献类型：
--
作者：
Gao Zhen;Kun Fang;Yanbo Zhu;Zhipeng Wang;Kai Guo
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.
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DOI：
10.3390/e23020221
发表时间：
2021-02-11
期刊：
Entropy (Basel, Switzerland)
影响因子：
--
作者：
Matilla-García M;Morales I;Rodríguez JM;Ruiz Marín M
通讯作者：
Ruiz Marín M
DOI：
--
发表时间：
2000-06
期刊：
--
影响因子：
--
作者：
C. Shively;R. Braff
通讯作者：
C. Shively;R. Braff
DOI：
10.3390/s18113800
发表时间：
2018-11-06
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Kim D;Cho J
通讯作者：
Cho J
影响因子：
4.9
作者：
Zhipeng Wang;Tinglin Li;Qiang Li;Kun Fang
通讯作者：
Zhipeng Wang;Tinglin Li;Qiang Li;Kun Fang
DOI：
10.1109/taes.2014.130498
发表时间：
2015-01-01
影响因子：
4.4
作者：
Cho, Jeongho;Yun, Youngsun;Heo, Moon-Beom
通讯作者：
Heo, Moon-Beom