新的离群可分性检验及其在GNSS定位中的应用

Jinling Wang, Nathan Knight
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引用次数: 29

摘要

随着测量、调整、异常值检测的自动化程度越来越高,以及结果在实时应用中的相应使用,需要可靠的方法来检测和减轻异常值。然而,人们经常建议,异常值检验不应被用作自动拒绝异常值的手段。其中一个原因是,离群值检测测试有时会识别出错误的测量。为了解决这一问题,本文提出了一种新的离群值可分离性检验,以确认通过离群值检验识别的离群值可以被自信地拒绝为离群值。此外,还得到了离群可分性检验的最小可分偏差和可分性乘因子。通过与重新应用全局模型检验方法和多重假设方法的初步比较,证明了新离群值可分性检验的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Outlier Separability Test and Its Application in GNSS Positioning
With the increasing automation of measurement, adjustment, outlier detection and the consequential use of the results for real-time applications, reliable methods to detect and mitigate an outlier are required. However, it is frequently advised that the outlier test should not be used as a means to automatically reject an outlier. One of the reasons for this is that the outlier detection test at times can identify a wrong measurement. To address this issue, this paper proposes a new outlier separability test to confirm that the identified outlier, by the outlier test, can be confidently rejected as the outlier. In addition, the Minimally Separable Bias and the separability multiplying factor are also obtained for the proposed outlier separability test. With the initial comparisons between the proposed method with the reapplication of the global model test method and multiple hypothesis method, the advantages of the new outlier separability test are demonstrated.
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