A Component-Level Model of Automatic Dependent Surveillance - Broadcast (ADS-B)

摘要

Automatic Dependent Surveillance - Broadcast (ADS-B) is being employed in numerous peer-to-peer initiatives attempting to expand the capacity of the National Airspace System (NAS) or enable mixed operations of manned and unmanned vehicles. Safety assessments of these initiatives rely, in part, on modeling the accuracy of ADS-B in reporting the position and direction of surrounding traffic. Frequently, these initiatives utilize a position uncertainty model that applies a reported ADS-B estimation position uncertainty (EPU) value to a Rayleigh distribution and uses a Gauss-Markov random walk to add error to the ADS-B output of a vehicle. This model of ADS-B state error is easy to implement and apply to numerous problems. However, it has a couple of drawbacks. First, the ADS-B state errors are equally probable in all directions. This is a good assumption in situations where aircraft maneuvering is not constrained. However, in situations such as landing where motion is constrained, the error distribution is likely to exhibit directionality and the non-directional model may skew results especially when assessing very low probabilities (e.g., 10-9) of catastrophic encounters. Second, the model does not account for processing latency in the receiving aircraft, which adds error as the receiver processes the data. This paper describes a component-level, end-to-end ADS-B error model that NASA Langley Research Center (LaRC) developed to investigate parallel approaches under instrument conditions.