KJCKorea
Journal Central

With the acceleration of Defense Innovation 4.0 and advancements in artificial intelligence and autonomous control technologies, the acquisition and fielding of Manned-Unmanned Teaming (MUM-T) have emerged as a critical agenda in defense capability development. However, existing effectiveness evaluation methodologies for defense acquisition and requirements validation remain heavily oriented toward conventional manned systems, focusing primarily on kinetic and physical combat metrics. Consequently, they fail to adequately quantify the inherent operational merits of unmanned systems, such as zero-casualty benefits, multidimensional autonomy levels, battlefield visualization enhancement, and network-centric synergies. This study aims to conduct a comparative analysis of effectiveness evaluation methodologies between manned and unmanned weapon systems and to propose an integrated, mission engineering-based multidimensional evaluation framework. To demonstrate the applicability of the proposed model, an illustrative case study of a coastal defense and complex anti-submarine/mine countermeasures scenario was analyzed using quantitative scenario data. The simulation results demonstrate that the MUM-T architecture drastically reduces human casualty risk by over 85% compared to manned-alone forces, while improving battlefield visualization by approximately 1.95 times through multi-domain sensor integration. Furthermore, the MUM-T configuration extended operational mission continuity fourfold and lowered total ownership costs by approximately 22%, confirming balanced operational utility under the simulated scenario conditions. The findings provide practical guidelines and analytical consulting criteria for defense requirement authorities, acquisition managers, and defense analysts in evaluating requirements and optimizing resource allocation.

Unmanned system threats are expanding from the air into the land, maritime, subsurface, and space domains, yet discussion of countermeasures has centered on aerial threats. An integrated Counter-Unmanned Systems (C-US) concept spanning multiple domains and a multi-domain threat classification framework have each been proposed. However, neither has addressed which component of the operational concept closes the capability gaps they identify. This conceptual study addresses that linkage problem. It first consolidates a classification framework built on four classification axes and the concept of cross-domain threats, refining the recording unit of the operational-domain axis and the definition of cross-domain relationships. The study then identifies four constraint-driven capability gaps, which arise when threat attributes structurally exclude available countermeasures. It maps these gaps to the four components of the C-US operational concept: multi-domain integrated command and control, organization, doctrine and procedures, and technical support. The result is a gap-response matrix. The analysis shows that three gaps can be addressed by specific components: the neutralization of RF-based countermeasures, the limits of simultaneous engagement, and the compounding of constraints. The gap arising from the difficulty of cross-domain handling, however, requires a multi-domain integrated command and control system. To allow future research to test these linkages, the study presents five testable propositions with falsification conditions, and the appendix contains a two-round Delphi survey design for evaluating them. Establishing expert consensus and empirically verifying response effectiveness remain tasks for future research.

This study identifies and prioritizes key development factors for an AI-based KCTC training system and proposes future development directions. Based on an analysis of the current KCTC system and domestic and international trends, 16 candidate factors for next-generation MILES and AI-based combat analysis systems were derived. A modified Delphi survey and Analytic Hierarchy Process (AHP) were conducted with 17 experts. The Delphi analysis identified 14 key factors, and the AHP results showed that next-generation MILES (.693) had a higher relative weight than AI-based combat analysis (.307). The five highest-ranked factors were an open and modular multi-network communication system (.198), data-driven engagement and damage assessment (.183), precise positioning and time synchronization (.117), spatiotemporal reconstruction and visualization of combat situations (.103), and integrated collection and management of multi-source and multimodal combat data (.092). These results indicate that reliable combat-data production is essential for the reliability and effective use of AI-based analysis. Accordingly, this study proposes developing next-generation MILES as a reliable combat-data production foundation and AI-based combat analysis as an intelligent analysis and feedback system through three stages: foundation building and limited validation, brigade-level integration and AI application, and advancement toward an intelligent integrated training system.