CURRENT STATE OF AUTOMATED FIRE CONTROL SYSTEMS FOR ARTILLERY SYSTEMS - RUBEL, YURII; HRYTSIUK, YURII (Herald of Khmelnytskyi National University. Technical sciences, 2025)
This study identifies the operational characteristics and development trends of contemporary Automated Fire Control Systems (AFCS) for artillery units, based on the analysis of both international and Ukrainian implementations. Using comparative analysis and a structured review of scientific literature, the research evaluates key performance indicators such as deployment time, targeting accuracy, degree of automation, and system interoperability with other weapon platforms.The study reveals that one of the leading trends in AFCS advancement is the integration of artificial intelligence (AI) for trajectory optimization and combat scenario forecasting. Consequently, these improvements enable a substantial reduction in response time and significantly enhance the accuracy of artillery fire.For example, systems like the German "MONARC," the Czech "PVNPG-14M," and the Ukrainian "Kropyva," "Delta," and "Combat" demonstrate marked improvements in tactical decision-making, precision targeting, and reaction speed during real-world combat engagements. Moreover, the use of portable and mobile platforms with embedded software–exemplified by Ukraine’s "Kropyva" system–proves effective in minimizing setup time and reducing the likelihood of human error in transmitting and processing fire control data.The analysis also identifies several critical limitations inherent in current AFCS, including high operator skill requirements, susceptibility to cyberattacks, and challenges in adapting to rapidly evolving electronic warfare environments. As a result, the study underscores the need for further development of cybersecurity protocols and interoperability standards aligned with NATO guidelines.The benefits of this study lie in defining promising directions for AFCS enhancement, such as deeper integration of AI algorithms, expanded cooperation with unmanned platforms, and a higher degree of automation in decision-making processes.
Keywords: automated artillery fire control; combat effectiveness; fire correction; network-centric interaction; machine learning algorithms; artificial intelligence