Risking to underestimate the integrity risk

Zaminpardaz, S, Teunissen, P and Tiberius, C 2019, 'Risking to underestimate the integrity risk', GPS Solutions, vol. 23, no. 2, pp. 1-1.

Document type: Journal Article
Collection: Journal Articles

Title Risking to underestimate the integrity risk
Author(s) Zaminpardaz, S
Teunissen, P
Tiberius, C
Year 2019
Journal name GPS Solutions
Volume number 23
Issue number 2
Start page 1
End page 1
Total pages 1
Publisher Springer
Abstract As parameter estimation and statistical testing are often intimately linked in the processing of observational data, the uncertainties involved in both estimation and testing need to be properly propagated into the final results produced. This necessitates the use of conditional distributions when evaluating the quality of the resulting estimator. As the conditioning should be on the identified hypothesis as well as on the corresponding testing outcome, omission of the latter will result in an incorrect description of the estimator's distribution. In this contribution, we analyse the impact this omission or approximation has on the considered distribution of the estimator and its integrity risk. For a relatively simple observational model it is mathematically proven that the rigorous integrity risk exceeds the approximation for the contributions under the null hypothesis, which typically has a much larger probability of occurrence than an alternative. Actual GNSS-based positioning examples confirm this finding. Overall we observe a tendency of the approximate integrity risk being smaller than the rigorous one. The approximate approach may, therefore, provide a too optimistic description of the integrity risk and thereby not sufficiently safeguard against possibly hazardous situations. We, therefore, strongly recommend the use of the rigorous approach to evaluate the integrity risk, as underestimating the integrity risk in practice, and also the risk to do so, cannot be acceptable particularly in critical and safety-of-life applications.
Subject Geomatic Engineering not elsewhere classified
Keyword(s) Conditional distribution
identification and adaptation (DIA)
DIA estimator
Integrity risk
Statistical testing
DOI - identifier 10.1007/s10291-018-0812-0
Copyright notice © 2019, The Author(s).
ISSN 1080-5370
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