Mathematical Model of an Information and Control System for Detecting Unmanned Aerial Vehicles and Loitering Munitions

Abstract

The article addresses the urgent scientific task of increasing the efficiency of detecting unmanned aerial vehicles (UAVs) and loitering munitions (LMs) under combat conditions by employing adaptive information-control systems (ICS) that integrate various types of sensors within wireless sensor networks (WSNs). The study analyzes modern approaches to the use of radar, electro-optical, and acoustic surveillance tools, particularly considering the results of deep learning and fusion algorithms. It is demonstrated that the integration of data from heterogeneous sensors ensures a high reliability of target detection even in the presence of interference, limited situational awareness, and maneuvering targets.

In the current context of countering high-tech threats, the application of fusion algorithms is especially relevant, as they enable the effective combination of information from diverse sensors. Such algorithms are a crucial component of ICS, as they allow for synergistic data processing, improve recognition reliability, and minimize false alarm rates even in complex combat environments.

A formalized probabilistic-adaptive model for detecting aerial objects is presented, which accounts for time constraints, sensor beam steering, network connectivity, and false alarm minimization. The proposed objective function enables optimization of the trade-off between detection speed and accuracy. Additionally, the article presents a mathematical framework that considers spatial constraints of the area of responsibility, predicted UAV trajectories, and their motion characteristics. The use of probability thresholds for decision-making enables the system to adapt to dynamic changes in the combat situation.

This article lays the foundation for the development of highly effective tactical-level ICSs that take into account real combat conditions and the need for rapid response.