Total system error classification and prediction based on logistic regression

doi:10.16265/j.cnki.issn 1003-3033.2020.12.015

Abstract

Abstract: In order to ensure that airborne warning system can send out correct alarm in time when TSE of flights is beyond safety limit, a LRM-based method to classify and predict TSE as in limit or over limit was proposed with influencing factors of TSE as sample characteristics of LRM. Experiments were conducted with stable factors, such as NSE, PDOP value and visible number of satellites selected as sample characteristics. Then, influence of LRM on classification and prediction accuracy of TSE was analyzed under conditions of different types and quantities of sample characteristics and combinations, and prediction accuracy and computational load were compared with that of traditional TSE estimation methods based on coordinate calculation. The results show that in the event of a combination of NSE, PDOP value and satellite visible number, LRM has highest prediction accuracy for TSE, and its calculation load is relatively small, which is better than that of TSE prediction methods based on coordinate calculation.

Key words: logistic regression method (LRM), total system error (TSE), navigation system error (NSE), position dilution of precision (PDOP), visible satellites number

CLC Number:

X949

JIAO Weidong, WANG Wei. Total system error classification and prediction based on logistic regression[J]. China Safety Science Journal, 2020, 30(12): 106-112.

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