A family of advanced weapon systems that deserves special attention comprises aerial autonomous weapons called Unmanned Combat Aerial Vehicles (UCAVs), which are characterized by the ability to loiter in the target area, sense the targets, acquire the targets, and then engage them. Modeling this combination of capabilities in a specific operational setting is necessary for addressing design and operational issues of this weapon. This work focuses on the development of an analytic probability model that captures key aspects of the autonomous weapon systems' engagement process. Special attention is given to simultaneous attack occurrences, imperfect battle damage assessment, and attack coordination properties. The model is a continuous-time Markov Chain and for its implementation a state generator and an algorithm that computes the transition and limiting probabilities has been developed and programmed in Java based software. The Markovmodel derives values for several measures of effectiveness (MOEs), and the average engagement time. Different operational scenarios and design configurations are examined in a sample analysis to demonstrate the model's capabilities. Tradeoffs among sensing, data processing capabilities, vulnerability and lethality of UCAVs are explicitly represented with respect to selected MOEs.
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