Concept of a Fire Control System for Anti-Aircraft Artillery with Elements of Artificial Intelligence

Abstract

This paper presents the concept and architecture of a Fire Control System (FCS) and the ballistic computation algorithms for anti-aircraft artillery. Traditional fire control systems rely on analytical methods that combine Kalman filter-based target tracking with physics-based projectile trajectory models to determine gun settings. We describe the ballistic computation pipeline: from target state estimation and firing zone analysis to interception point calculation and final gun settings determination. The architecture encompasses measurement processing, Kalman filtering for multiple target motion hypotheses, and ballistic algorithms that account for atmospheric conditions and projectile dispersion. We establish the mathematical foundations for the definition of the fire mission, present the algorithmic procedures for the calculation of the dynamic firing engagement zone, and discuss the integration of hit probability methods for improved mission effectiveness. This work provides the foundation for understanding the decision-making challenges in modern anti-aircraft fire control and motivates the need for intelligent, adaptive firing timing strategies; these are addressed in a follow-up paper devoted to reinforcement learning for decision-making support in complex fire control environments.