Outlier Identification and Correction Online for the Ocean Geomagnetic Parameter Measurement

Abstract:

The outlier identification and correction of the geomagnetic parameter measurement is an important influencing factor for the probability and precision of the matching algorithms. The ream-time measurements of the geomagnetic field parameter can be considered as the first-order non-stationary random process, the real-time measurement value can be predicted by using the multi-level recursive method, the outlier identification can be done by the Dixon criterion, and the correction can be made by using the median filter and the one step prediction value. The inspection is carried out by using the emulation data and actual data, the results show that not only the isolated type but also the spot-type outlier value can be detected well and the signal reconstruction error is less than 5%.

• Periodical: Applied Mechanics and Materials (Volumes 90-93)
• Pages: 2822-2827
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.90-93.2822
• Online since: September 2011
• Authors: Zun Yi Xu, Dong Wei, Lei Yan
• Keywords: ARMA Model, Dixon Criterion, Geomagnetic Parameter Measurement, Multi-Level Recursive Method, Outlier Identification

References

[1] Yuntao Yang, Zhiyong Shi, Zhenzhen Guan. Application of Geomagnetic Field in Navigation and Localization System. Journal of Chinese Inertial Technology, Vol.15 No.6 (2007), pp.686-692. (In Chinese)

[2] Yanling Hao, Yafeng Zhao, Junfeng Hu. Preliminary Analysis on the Application of Geomagnetic Field Matching in Underwater Vehicle Navigation. Progress in Geophysics, Vol.23 No.2 (2008), pp.594-598. (In Chinese)

[3] Fei Liu，XianGao Zhou，Ye Yang. Geomagnetic Matching Location Using Correlative Method. Journal of Chinese Inertial Technology, 2007, 15(1):47-50. (In Chinese)

[4] Yi Lin, Lei Yan, Yuefeng Liu, Qingxi Tong.Offline outlier identification for dynamic measurements in underwater geomagnetism navigation. Proceeding of the 7th World Congress on Intelligent Control and Automation,Pages:5009-5013,2008.

DOI: 10.1109/wcica.2008.4593740

[5] Ronald K.Pearson. Outliers in Process Modeling and Identification. IEEE Transactions on Control Systems Technology, Vol.10,No.1,Pages:55-63, 2002.

[6] Jie Ma, Di Li, Shaohong Wang, Xiaoli Xu. Data-based Adaptive Fault Prediction Method and Its Application. The Ninth International Conference on Electronic Measurement & Instruments, ICEMT' 2009, P:4-1013～4-1016.

DOI: 10.1109/icemi.2009.5274148