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

doi:10.11947/j.AGCS.2017.20160079

Abstract

Abstract: 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.

Key words: multiple satellite faults, RAIM, faults detection, independent component analysis, time series

CLC Number:

P228

ZHANG Qianqian, GUI Qingming, GONG Yisong. Multiple Satellite Faults Detection and Identification Based on the Independent Component Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(6): 698-705.

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