DYNAMIC BAYESIAN DECISION MODELING FOR AUTONOMOUS UAV ENGAGEMENT UNDER CONDITIONS OF UNCERTAINTY

Emanuelle PERȚA, Alina TOTOI

Abstract


Autonomous unmanned aerial vehicles (UAVs) operate under significant uncertainty, where engagement decisions involve asymmetric and time-dependent costs. This paper proposes a Bayesian decision-theoretic framework for autonomous UAV engagement, explicitly modeling uncertainty, delay penalties, and abort options within an expected utility formulation. The decision problem considers three actions: engage, wait, and abort. It derives optimal policies as functions of target legitimacy probability and cost parameters. A numerical analysis based on synthetic simulations explores the decision space and identifies distinct operational regimes. The results demonstrate that delaying action can be optimal over broad regions of the parameter space, particularly under high uncertainty or moderate delay costs, highlighting the importance of risk-aware and temporally adaptive decision-making in autonomous UAV systems.

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