COMPUTATIONALLY EFFICIENT ASSESMENT OF FIGHTER AIRCRAFT MISSION SURVIVABILITY WITH PROBABILISTIC GRAPHICAL MODELS
This paper proposes a probabilistic model for assessment of fighter aircraft mission survivability. Mission survivability analysis is a critical phase for both design of the fighter aircraft and evaluation of its performance. The standard deterministic performance metrics such as aircraft agility and manoeuvrability are not sufficient to measure mission survivability, since the probability of aircraft surviving the missions depends heavily on lethality and position of threats, such as surface to air missile systems. Since the dynamics and parameters of the threats are mostly uncertain, previous works proposed several different probabilistic models for modelling the mission survivability of fighter aircraft under uncertain threats. However, most of the existing models either oversimplify the problem or leads to in complicated high dimensional probability distributions, which are unfeasible for evaluation of large-scale missions. In this study, we fuse the threat and sensor models from several existing works and show that the mission survivability can be modelled as a probabilistic graphical model, which enables rapid sampling and Monte Carlo evaluation of survivability, even on large-scale missions that involve many different threat and sensor networks. In addition, we show that graphical representation can also be used for addressing the inverse problem of determining required aircraft performance parameters for a specified survivability rate.
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