Multiple Satellite Faults Detection and Identification Based on the Independent Component Analysis

被引：0
|
作者：
Zhang Q. [1 ,2 ]
Gui Q. [2 ]
Gong Y. [1 ]
机构：
[1] Institute of Aerospace Surveying and Mapping, Beijing
[2] Institute of Science, Information Engineering University, Zhengzhou
来源：
| 1600年 / SinoMaps Press卷 / 46期
基金：
中国国家自然科学基金;
关键词：
Faults detection; Independent component analysis; Multiple satellite faults; RAIM; Time series;
D O I：
10.11947/j.AGCS.2017.20160079
中图分类号：
学科分类号：
摘要：
Considering that the independent component is sensitive to outliers, we propose an algorithm for faults detection in multivariate pseudorange time series based on independent component analysis (ICA). The threshold for outlier detection is determined through the Chebyshev inequality. Then we introduce the interventional model of time series to estimate the magnitudes of the potential satellite faults, and finally the satellite faults are identified based on the 3σ principle. In order to meet the real time requirement of receiver autonomous integrity monitoring (RAIM), a sliding window is used to transform the fault detection algorithm of the batch process into a real time one. Furthermore, a new algorithm for on line detection and identification of multiple faults is designed, and then the implementation process of the new RAIM algorithm is given. We validate the new algorithm by the civil data from 5 iGMAS monitoring stations of BeiDou in China. Examples illustrate that the new algorithm is effective in handling multiple satellite faults in real time, and the correct detection probability of faults is higher than that of the existed RANCO algorithm. © 2017, Surveying and Mapping Press. All right reserved.
引用
收藏
页码：698 / 705
页数：7
相关论文
共 37 条
  • [1] Hewitson S., Wang J.L., GNSS Receiver Autonomous Integrity Monitoring (RAIM) for Multiple Outliers, Journal of Navigation, 4, 4, pp. 47-54, (2006)
  • [2] Knight N.L., Wang J., Rizos C., Et al., GNSS Integrity Monitoring for Two Satellite Faults, Proceedings of the IGNSS Symposium, (2009)
  • [3] Ni J., Zhu Y., Guo W., An Improved RAIM Scheme for Processing Multiple Outliers in GNSS, Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), pp. 840-845, (2007)
  • [4] Bi J., Li W., Gu S., Precise Point Positioning RAIM Method, Acta Geodaetica et Cartographica Sinica, 40, pp. 63-67, (2011)
  • [5] Wang J., Wang J., Mitigating the Effect of Multiple Outliers on GNSS Navigation with M-estimation Schemes, Proceedings of the International Global Navigation Satellite Systems Society (IGNSS) Symposiumm, pp. 1-9, (2007)
  • [6] Zhang Y., Wu F., Isshiki H., A New Cascade Method for Detecting GPS Multiple Outliers Based on Total Residuals of Observation Equations, Proceedings of the 2012 IEEE/ION Position Location and Navigation Symposium, pp. 208-215, (2012)
  • [7] Tong H., Zhang G., Robust Positioning Algorithm with Modified M-estimation for Multiple Outliers, Acta Geodaetica et Cartographica Sinica, 43, 4, pp. 366-371, (2014)
  • [8] Schroth G., Ene A., Blanch J., Et al., Failure Detection and Exclusion via Range Consensus, Proceedings of the ENC GNSS Conference, (2008)
  • [9] Schroth G., Rippl M., Ene A., Et al., Enhancements of the Range Consensus Algorithm (RANCO), Proceedings of the 21st ION GNSS Conference, (2008)
  • [10] Rippl M., Schroth G., Belabbas B., Et al., A Probabilistic Assessment on the Range Consensus(RANCO) RAIM Algorithm, Proceedings of the ION ITM, pp. 248-255, (2009)
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