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Abstract: This paper examines how to evaluate observed instantaneous signal‐in‐space errors and determine suitable upper bounds on their likely distribution. We compare this performance against the commitments and broadcast values from the satellites to determine whether the provided values are sufficient or not. An important aspect is to characterize the errors in light of known or predictable characteristics. Oftentimes, errors are grouped together to create a single averaged distribution. However, there may be times and conditions where performance is notably worse. We need to separate out such conditions and evaluate the distributions individually so as not to form overly optimistic estimates of the error bounds. Further, we must ensure that these parameters will continue to bound future fault‐free behavior. We will describe the conservative steps taken in the estimation process and the validation effort, both with the real data and versus the stated commitments from the constellation service providers. Copyright © 2018 Institute of Navigation [ABSTRACT FROM AUTHOR]

This paper describes an efficient new algorithm being developed for the Wide Area Augmentation System (WAAS) that significantly improves its ability to detect threats arising from multiple simultaneous one to two sigma errors. This new algorithm is based on the sum of the squares of the normalized errors at each reference station. It has significantly improved the ability to detect error conditions that are not obviously faulted individually, but that could be harmful to users when combined. Currently, other monitors are inflating the confidence bounds to protect against such conditions. This new User Position Monitor (UPM) is able to identify such threats, and as a consequence, excess conservatism may be removed from the system. We expect this innovation to lead to better availability while maintaining equal or better levels of integrity. Copyright © 2017 Institute of Navigation [ABSTRACT FROM AUTHOR]

One of the most commonly used detection statistics in autonomous integrity monitoring is the solution separation statistic. In this paper, we show that the solution separation statistic is closely linked to the optimal detection statistics. More precisely, we show that in the case of one threat, even multi-dimensional, the optimal detection statistic is the solution separation, and that in the case of many one-dimensional threats, the optimal statistics can be expressed as a function of the solution separations corresponding to each threat. This is achieved by casting the search of the optimal detection region as a mini-max problem and by using the Neyman-Pearson lemma to limit the search of the detection regions to a class of regions parameterized by a bias. These results allow us to establish a lower bound on the minimum achievable integrity risk. Copyright © 2017 Institute of Navigation [ABSTRACT FROM AUTHOR]