Probabilistic Safety Assessment for UAS Separation Assurance and Collision Avoidance Systems

摘要: The airworthiness certification of aerospace cyber-physical systems traditionally relies on the probabilistic safety assessment as a standard engineering methodology to quantify potential risks associated with faults in system components. This paper presents and discusses detect avoid (DAA) relying multiple cooperative non-cooperative tracking technologies identify risk collision unmanned aircraft (UAS) other flight vehicles. In particular, fault tree analysis (FTA) is utilized measure overall unavailability for each basic component failure. Considering inter-dependencies navigation surveillance systems, common cause failure (CCF)-beta model applied calculate failures. Additionally, an importance conducted measures most significant Results indicate that traffic detection by contribute more DAA functionality probability ownship locatability greater than its function. Although all sensors individually yield 99.9% operational availability, implementation adequate multi-sensor both shown be necessary achieve desired levels possible encounters. These results strongly support adoption unified analytical framework cooperative/non-cooperative UAS elicits evolution current properly account artificial intelligence machine-learning based systems.