Target Assignment and Fire Scheduling Method for Unmanned Combat Systems

doi:10.3969/j.issn.1009-086x.2026.02.006

Abstract

Abstract:

To address the real-time and efficiency requirements for multi-target cooperative interception by unmanned combat systems in dynamic battlefield scenarios， this paper proposes a greedy target assignment and fire scheduling method based on dynamic time window （GTAFS-DTW）. By constructing a relative motion model between combat units and targets， the proposed method incorporates engagement distance constraints， task execution constraints， and target maneuver characteristics. a dynamic time window screening mechanism is designed with the optimization objective of minimizing engagement time and impact time difference. On this basis， a hierarchical greedy strategy is adopted to prioritize unit-target pairs with overlapping feasible engagement intervals， and temporal cooperative optimization is achieved through iterative adjustment of window boundaries and fire scheduling. Simulation results demonstrate that GTAFS-DTW effectively reduces impact time deviation to within 0.5 s in both lateral and longitudinal queue scenarios compared with traditional discrete particle swarm optimization （DPSO）， significantly enhancing computational efficiency and satisfying real-time optimization requirements.

Key words: unmanned combat systems, target assignment, fire scheduling, freedy algorithm, time coordination, particle swarm optimization（PSO）

摘要：

针对无人值守作战系统在动态战场环境下多目标协同拦截的实时性与高效性需求，提出一种基于动态时间窗口的贪心目标分配与发射时序规划方法（greedy target assignment and fire scheduling method based on dynamic time window, GTAFS-DTW）。通过构建作战单元与目标的相对运动模型，结合攻击距离约束、任务执行约束及目标运动特性，以最小化攻击时间与命中时间差为优化目标，设计动态时间窗口筛选机制。在此基础上，采用分层贪心策略，优先选择可攻击时间区间交集的作战单元-目标对，并通过迭代调整窗口边界及发射时序规划实现时序协同优化。仿真实验表明，相较于传统离散粒子群（discrete particle swarm optimization，DPSO）算法，GTAFS-DTW在横向队列与纵向队列场景下均能够将命中时间差减小至0.5 s以内，降低了目标机动响应窗口，同时显著提升了计算效率，满足在线任务规划的实时性要求。

关键词: 无人值守作战系统, 目标分配, 发射时序规划, 贪心算法, 时间协同, 粒子群算法

CLC Number:

E917

Peilin LI, Jie GUO, Xifeng CHEN, Yangyang WAN, Shengjing TANG. Target Assignment and Fire Scheduling Method for Unmanned Combat Systems[J]. Modern Defense Technology, 2026, 54(2): 69-81.

李沛璘, 郭杰, 陈西峰, 万泱泱, 唐胜景. 无人值守作战系统目标分配与发射时序规划方法[J]. 现代防御技术, 2026, 54(2): 69-81.

Figures/Tables 20

Fig. 1 Combat scenario of combat unit

Fig. 2 Relative motion relationship between combat units and targets

Fig. 3 Feasible engagement time set of combat units

Fig. 4 Flowchart of the greedy task planning algorithm based on dynamic time window

Fig. 5 Initial deployment against lateral queue

Table 1 Task assignment results against lateral queue

作战单元编号	分配目标编号	发射时间/s
13	4	7.743 4
14	3	7.788 1
15	2	7.576 5
16	1	7.268 8

Fig. 6 Trajectories of combat units and targets against lateral queue without fire scheduling

Fig. 7 Trajectories of combat units and targets against lateral queue with fire scheduling

Fig. 8 Launch and attack timing sequence of combat units against lateral formation

Table 2 Engagement results against the lateral queue

作战单元编号	攻击目标（时序规划）	攻击目标（无时序规划）	命中时间/s（时序规划）	命中时间/s（无时序规划）
13	4	4	8.514 0	6.720 3
14	3	4	8.551 4	7.349 2
15	2	2	8.829 4	8.666 0
16	1	1	8.945 1	9.197 8

Fig. 9 Initial deployment against the longitudinal queue

Table 3 Task assignment results against longitudinal queue

作战单元编号	分配目标编号	发射时间/s
6	1	9.213 1
10	2	9.220 1
13	3	8.514 8
14	4	8.565 0

Fig. 10 Trajectories of combat units and targets against longitudinal queue without fire scheduling

Fig. 11 Trajectories of combat units and targets against the longitudinal queue with fire scheduling

Fig. 12 Launch and attack timing sequence of combat units against longitudinal formation

Table 4 Engagement results against the longitudinal queue

作战单元编号	攻击目标	命中时间/s （时序规划）	命中时间/s （无时序规划）
6	1	9.808 2	10.819 9
10	2	9.779 7	9.953 1
13	3	9.753 5	11.124 6
14	4	9.820 0	10.461 7

Fig. 13 Comparison of computation time

Fig. 14 Comparison of objective function values

Fig. 15 Memory usage comparison

Fig. 16 Trajectories of combat units and targets （DPSO）

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