TY  - JOUR
T1  - A Survey on Weapon-Target Assignment for Realistic Battlefield Environments: From Exact Algorithm to Deep Reinforcement Learning
AU  - Eom, Chanin 
AU  - Lee, Jaehwi 
AU  - Kwon, Minhae 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.2.205
KW  - Military decision-making
KW  - Weapon-target assignment
KW  - Intelligent command decision
KW  - Deep reinforcement learning
KW  - Heuristic algorithm
AB  - As the demand for intelligent command decision support systems grows, significant attention has been directed toward military decision-making. Weapon-target assignment (WTA) is a key component of a commander’s decision-making process, playing a crucial role in executing effective attacks and efficiently managing resources.
Recently, WTA research has evolved to address realistic modern battlefield environments, increasing the complexity of optimization. For this reason, much WTA research has focused on time-efficient approaches, e.g., heuristic algorithms or deep reinforcement learning. Among these methods, deep reinforcement learning has garnered remarkable attention due to its high generalization performance in complex environments. In this paper, we survey research trends in WTA studies, a key component of intelligent military decision-making. Furthermore, we propose future directions for enhancing WTA systems to better address realistic battlefield environments.