EVALUATION OF MORTAR UNIT PROTECTION IN A MULTISPECTRAL BATTLEFIELD ENVIRONMENT USING ARTIFICIAL INTELLIGENCE

This paper examines the protection of mortar units under contemporary battlefield conditions characterized by persistent multispectral surveillance and shortened counter-battery reaction cycles. The study aims to evaluate the effectiveness of selected camouflage measures and decoy assets and to assess their impact on reducing the probability of detection and subsequent destruction of firing positions. The research methodology integrates a field experiment focused on object detectability using optical, multisensor, and unmanned aerial observation systems with an analytical framework based on the development of a threat register and a risk matrix. This approach enables systematic evaluation of key asset exposure to diverse threat categories and supports quantification of overall operational vulnerability. The results demonstrate that isolated static camouflage measures exhibit limited effectiveness in a multispectral environment. In contrast, a coordinated combination of terrain exploitation, decoy deployment, and temporally constrained occupation of firing positions significantly reduces detection and engagement risk. The study concludes that effective protection of mortar units in modern conflicts depends not on absolute concealment, but on controlled deception and structured operational risk management. The findings provide a transferable framework applicable to planning and execution of fire support operations under contemporary battlefield conditions.