Assessing the Effect of ADS-B Message Drop-Out in Detect and Avoid of Unmanned Aircraft System Using Monte Carlo Simulation

Abstract

This work analyzes the severity and risk associated with automatic dependent surveillance-broadcast (ADS-B) message drop-out in detect and avoid (DAA) function of unmanned aircraft systems (UAS). Performance assessment of the universal access transceiver (UAT) ADS-B message implies that, in some cases, ADS-B fails to update within a specified update interval, which is referred to as ‘drop-out’ in this work. ADS-B is a fundamental surveillance sensor for both class 1 and class 2 DAA systems. Message loss or drop-out has been found as one of the common limitations of the ADS-B system. The key feature of this study is incorporating the update rate of real ADS-B data transmitted from the manned aircraft. The data were received from the Grand Forks International Airport, North Dakota. Monte Carlo method has been adopted to resolve encounter scenarios in the presence of drop-out. The change in the alert triggered by the UAS DAA in the presence of ADS-B drop-out has been investigated. Furthermore, the risk matrices are created to quantify the associated risk with drop-out affected alerts. Simulation results depict that both the duration of drop-out and DAA look-ahead time affect the alert-triggering function of UAS. With a small look-ahead window and longer duration of drop-out, the number of warning alerts increases. Also, alerts are affected more during an overtaking encounter than that of a head-to-head encounter. A system-level analysis is also carried out to recognize the potential reasons behind the ADS-B drop-out.