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