Scientific and methodological approaches to increasing the efficiency of the use of guided aircraft bombs through visualization of the targeting process

Abstract

This paper presents a scientific rationale for enhancing visualization efficiency during the targeting process of guided aircraft bombs (CAB). It has been determined that targeting visualization is a critical element of the combat cycle: it provides detection, recognition, tracking, and precise guidance of the munition on the target. The general trend, based on the analysis of domestic and foreign publications, is the transition from passive systems to active, multi-spectral platforms capable of operating in conditions of interference, nighttime, and complex terrain. Special attention is paid to image stabilization algorithms, automatic target recognition, and integration with trajectory correction systems. The study analyzes the impact of sensor type, weather conditions, platform stability, frame rate, and image contrast on strike accuracy. A comprehensive methodology is proposed, incorporating adaptive filtering, automatic target recognition, image stabilization, and integration with navigation systems. Visualization of aiming provides a comprehensive improvement of the combat characteristics of the KAB guidance systems. Its implementation allows to increase the accuracy of the hit, reduce the operator's reaction time and minimize the risks of erroneous actions. Experimental modeling confirms the effectiveness of multispectral and hyperspectral sensors, as well as the utility of CLAHE, Kalman filters, and CNN-based algorithms. The proposed aiming and navigation system is designed to increase the accuracy of aiming during an attack with a UMPK on ground targets in the absence of visual contact with the target and to expand the capabilities of navigation support in flight. The findings support the modernization of aerial platforms, the development of autonomous guidance systems, and the formulation of doctrinal standards for the Armed Forces of Ukraine