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A multi-type target collaborative allocation method with temporal constraints
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DOI：10.1016/j.iswa.2026.200665.png)
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Abstract
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This paper addresses the challenge of target allocation under temporal constraints in scenarios involving multiweapon coordination against multiple target types. It overcomes technical limitations of traditional planning methods, such as handling sequential decision-making, dynamic responses, and coupling relationships, by designing a dynamic target allocation framework based on a dueling deep Q-network with target compression and segmented decision-making. First, for a typical combat scenario in which a ground-based unmanned formation engages three types of targets using two kinds of artillery shells, a damage mechanism model incorporating temporal dependencies is constructed. Second, using a multi-dimensional state space to encode both munitions inventory and target states, a segmented decision mechanism is established to achieve joint optimization of the allocation matrix and strike sequence. Finally, a target compression module is introduced to resolve strategy stability issues within the high-dimensional action space. Simulation experiments demonstrate that the proposed method significantly improves strike effectiveness while satisfying temporal constraints, offering a novel technical pathway for intelligent target allocation.
Keywords:
Target allocation
Temporal constraints
Deep reinforcement learning
Segmented decision-making
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