An Anomaly Detection and Correction Method Based on Measurement Representation Model

Abstract:

Tracking data containing outliers would seriously distort the accuracy of data processing for space tracking ships. Effective measures have to be adopted for the detection and correction of outliers. The time series of observations obtained by space tracking ships contain complex periodical elements caused by ship deformation and rolling. It brings difficulty to the detection of outliers. To solve the problem, a variety of detection methods were studied based on measurement elements representation model. The paper gives an analysis about the characteristic of measurements taken by the ships and proposes to detect the information about outliers by considering the correlation in time, space and data properties. A detection model based on spline function representation model is constructed and tested. Numerical results show that the proposed method can detect and correct outliers effectively, and provide an alternative to the current method of the measurements processing for the space tracking ships.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 51-57
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.51
• Online since: July 2013
• Authors: Wei Kang Zhu, Hui Fen Li, Jun Shan Mu, Guo Hu Xue, Zheng Xu Dai
• Keywords: Detection Model, Gross Error, Measurement Result, Representation Model, Spline Function

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