Integrity for Non-Aviation Users
July 1, 2011 By: Sam Pullen, Todd Walter, Per Enge GPS WorldMoving Away from Specific Risk
Non-aviation users of satellite- and ground-based augmentation systems do not require the conservative level of integrity built into these systems for aviation users. Removing it can produce substantial benefits in terms of smaller error bounds and improved availability.
Both space-based and ground-based augmentation systems (SBAS and GBAS, respectively) are designed to enhance standalone GNSS navigation to meet the requirements of civil aviation. SBAS and GBAS corrections and integrity information are also available to the non-aviation user population, such as automobiles, buses, and trains on land as well as ships near shore. This much larger user base can benefit as much from the integrity components of SBAS and GBAS as from the increased accuracy obtained from applying SBAS and GBAS pseudorange corrections. However, there are significant differences between the aviation interpretation of navigation integrity and the interpretation that would be natural to most users.
SBAS and GBAS provide integrity in a multi-step procedure that is laid out in the RTCA Minimum Operational Performance Standards (MOPS) for the FAA versions of both systems: DO-229D for the Wide Area Augmentation System (WAAS) and DO-253C for the Local Area Augmentation System (LAAS). These systems indicate which ranging measurements should be excluded as unsafe to use and provide bounding error standard deviations, or sigmas, for the remaining usable measurements. Each aircraft uses this information to compute vertical and horizontal protection levels that define position-domain error bounds at desired probabilities. This process is straightforward, logical, and is not limited to aviation users. However, the requirements and assumptions underlying it make it very conservative.
SBAS and GBAS are designed to meet integrity requirements defined in terms of what is known as specific risk. Briefly, this means that all safety requirements must be met for the worst combination of knowable or potentially foreseeable circumstances under which an operation may be conducted. Some variable factors important to safety, such as the user’s satellite geometry, are known by definition. Others, such as receiver thermal noise, are random and unpredictable. But several factors that are critical to GNSS performance, such as multipath and ionospheric errors, are neither completely random nor deterministic. Specific risk typically treats all error sources that are not completely random in a worst-case manner. SBAS and GBAS are designed to mitigate specific risk to support civil aviation, and the resulting conservatism makes SBAS and GBAS less attractive to non-aviation users who expect tighter protection levels relative to nominal system accuracy.
Fortunately, non-aviation users need not apply all MOPS procedures required of aviation users if their own safety requirements differ. Most users define integrity in average or ensemble terms, meaning that everything not known in practice is treated as random and is probabilistically mixed (or convolved) together. The protection levels valid for these users would be much lower than for aviation users, even though the stated bounding probability is the same. This contrast is illustrated in Figure 1, which shows example bounds on 2-D vertical errors at a probability of 0.95 (the 95th percentile, or 95 percent) for accuracy and a probability of 1–10-7 for integrity. The term VPE stands for vertical position error, while VPL stands for vertical protection level. Analogous terms (HPE and HPL) and a similar picture exist in two dimensions for horizontal errors.
Figure 1. Illustration of 95 percent accuracy bounds and 1–10-7 protection levels.
Only one 95 percent error bound is shown in Figure 1 because this probability can be observed, estimated, and modeled with theory and reasonable amounts of data (hundreds or thousands of independent samples). This is not at all the case at the very small probability of 10-7 that applies to aviation precision approach: it is roughly equivalent to one event in 47.5 years per 150-second precision-approach interval. Both theory and data fall far short of being able to predict such rare-event errors. Extrapolating from available data to 1–10-7 using Gaussian distributions is perilous because the Gaussian distribution almost never applies at such small probabilities. Mixed-Gaussian models, other so-called fat-tailed distributions, and inflation of Gaussian parameters help address this, but the uncertainty regarding the true error distribution results in significantly different error bounds depending on the assumptions that are made. The same is true regarding the effects of faults and anomalies that are more probable than 10-7 but are still rare and poorly understood.
In the end, different means of assessing these uncertainties and various degrees of user risk aversion result in different 1–10-7 protection levels, as shown in Figure 1. It is this difference that we wish to quantify and exploit in this article.
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