Safety concerns are progressively emerging regarding the adoption of Unmanned Aircraft Systems (UASs) in diverse civil applications, particularly within the booming air transportation system, such as in Advanced Air Mobility. The outcomes of risk assessment determine operation authorization and mitigation strategies. However, civil UAS operations bring novel safety issues distinct from traditional aviation, like ground impact risk, etc. Existing studies vary in their risk definitions, modelling mechanisms, and objectives. There remains an incomplete gap of challenges, opportunities, and future efforts needed to collaboratively address diverse safety risks. This paper undertakes a comprehensive review of the literature in the domain, providing a summative understanding of the risk assessment of civil UAS operations. Specifically, four basic modelling approaches utilized commonly are identified comprising the safety risk management process, causal model, collision risk model, and ground risk model. Then, this paper reviews the state of the art in each category and explores the practical applications they contribute to, the support offered to participants from multiple stakeholders, and the primary technical challenges encountered. Moreover, potential directions for future work are outlined based on the high-level common problems. We believe that this review from a holistic perspective contributes towards better implementation of risk assessment in civil UAS operations, thus facilitating safe integration into the airspace system.

Contingency management of Unmanned Aircraft System (UAS) is an essential part of UAS Traffic Management (UTM). One of such contingencies is the loss of positional information due to weak or loss of Global Positioning System (GPS) signals, which will compromise the safety of the overall UTM system. This paper investigates the UAV trajectory estimation problem when GPS signal is weak or unavailable in urban environments. This research serves two purposes: Firstly, the study employed Extended Kalman Filter (EKF) to estimate the UAV true trajectory when GPS signal is intermittent or unavailable; Secondly, this work analyzed the deviations between pre-defined trajectory and estimated ones in different signal scenarios. The simulation results show that: (1) for uniform distribution of GPS signal, the estimated trajectory will deviate linearly with the decrease of signal density up until a density of 0.1hz and increase exponentially after that; and (2) for non-uniform distribution, the Gaussian distribution performed the worst, followed by random distribution and Sine distribution.