Deriving a frequentist conservative confidence bound for probability of failure per demand for systems with different operational and test profiles

Reliability testing is typically used in demand-based systems (such as protection systems) to derive a confidence bound for a specific operational profile. To be realistic, the number of tests for each class of demand should be proportional to the demand frequency of the class. In practice however, the actual operational profile may differ from that used during testing. This paper provides a means for estimating the confidence bound when the test profile differs from the profile used in actual operation. Based on this analysis the paper examines what bound can be claimed for different types of profile uncertainty and options for dealing with this uncertainty. We also show that the same conservative bound estimation equations can be applied to cases where different measures of software test coverage and operational profile are used

Instantaneous ambiguity resolution in global-navigation-satellite-system-based determination applications: A multivariate constrained approach

Carrier phase integer ambiguity resolution is the key to high-precision Global Navigation Satellite System (GNSS) positioning, navigation, and attitude determination. It is the process of resolving the unknown cycle ambiguities of the carrier phase data as integers. After ambiguity resolution, precise baseline estimates become available, which can be used to derive the attitude of a multi-antenna platform. The purpose of this contribution is to present and test a rigorous GNSS-based attitude determination method, optimally exploiting the complete set of geometrical constraints. The key to this new method is an extension of the popular LAMBDA method: the multivariate constrained LAMBDA. The method estimates the integer ambiguities and the platform’s attitude in an integralmanner, fully exploiting the known body geometry of the multi-antenna configuration. As a result, the ambiguity resolution performance is greatly improved. The method is extensively tested addressing the most challenging scenario: single-epoch single-frequency GNSS observations are processed without any filtering, external aid, or dynamic modeling