Illustrative presentation of some basic concepts of network internal reliability with a focus on engineering surveys

Witold Prószyński


The paper integrates some earlier and the recent findings of the author in the area of network internal reliability and presents a consistent system of concepts in this respect. The concepts of outlier detection and outlier identification linked directly with the global model test and the outlier tests respectively, are shown as a basis for the concepts such as outlier detectability and outlier identifiability. Also, a four level classification of gross errors expressed in a form of a tree-diagram is presented including perceptible and imperceptible errors, detectable and undetectable errors and identifiable and unidentifiable errors. Their properties are given mainly in a descriptive way, deliberately limiting rigorous mathematical formulas to a necessary minimum. Understanding of different types of gross errors is useful in analyzing the results of the outlier detection and identification procedures as well as in designing the networks to make them duly robust to observation gross errors. It is of special importance for engineering surveys where quite often low-redundancy networks are used. Main objective of the paper is to demonstrate a clear and consistent system of basic concepts related to network internal reliability.
Author Witold Prószyński (FGC / DEIS)
Witold Prószyński,,
- Engineering Geodesy and Control Surveying Systems
Journal seriesReports on Geodesy and Geoinformatics, ISSN 2391-8365, e-ISSN 2391-8152
Issue year2016
Publication size in sheets0.5
Keywords in Englishunit redundancy; low-redundancy networks, detectability, identifiability, gross error classification
Internal identifier99/2016
Languageen angielski
2016_Prószyński_Illustrative_presentation_99.pdf 529.21 KB
Additional file
2016_Oświadczenie_Prószyński_Illustrative_presentation_99.pdf 687.19 KB
Score (nominal)13
ScoreMinisterial score = 13.0, 28-11-2017, ArticleFromJournal
Ministerial score (2013-2016) = 13.0, 28-11-2017, ArticleFromJournal
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