Year: 2026
Type: article
Abstract: This paper presents analytical solutions for evaluating the effectiveness of multiple weapons against point targets and two types of area targets: those with uniformly distributed elements in a rectangular area and those with normally distributed elements. These solutions incorporate three novel attack methods, each distinguished by unique aiming and firing mechanisms that assign multiple weapons to each aim point, reflecting more realistic elements such as dependent engagements, independent and dependent delivery errors, and the use of multiple launchers. Unlike previous studies, which were limited to simplified scenarios such as multiple weapons targeting a single aim point or a single weapon per aim point, this research emphasizes multiple weapon assignments per aim point to improve the accuracy of probabilistic modeling. Although the analytical expressions involve finite binomial-type combinatorial expansions that are difficult to analyze directly, numerical evaluations based on these solutions demonstrate greater efficiency compared to stochastic simulations, accounting for factors such as accuracy, lethality, and aim point distributions across different target types. These deterministic methods also facilitate more effective applications in aim point optimization and weapon-target assignment problems. To highlight this, the paper compares computational times for expected fractional damage calculations using the proposed solutions with those from Monte Carlo simulations for the same scenarios. Additionally, a numerical technique based on the analytical solutions is introduced to derive optimal aim point distributions that maximize damage probability or expected fractional damage, demonstrated through examples in artillery contexts. These analytical solutions highlight the critical role of strategic aim point distribution in multiple-weapon engagements, where success depends on both the precision of individual weapons and the optimized spatial arrangement of aim points to maximize damage potential.
Source: Soft Computing
Authors: Sei-Hoon Moon, Hun Ji, Surin Kim, Kyeongsoo Lee
Institution: Agency for Defense Development
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Funder: Agency for Defense Development
Award: G8533990894
OpenAlex ID: W7204457782