基于拦截捕获区的“多对多”能量最优目标分配方法

李昊键; 黎克波; 梁彦刚

doi:10.13700/j.bh.1001-5965.2024.0330

基于拦截捕获区的“多对多”能量最优目标分配方法

doi: 10.13700/j.bh.1001-5965.2024.0330

李昊键^{1, 2},
黎克波^{1, 2, ,},
梁彦刚^{1, 2}

1.
国防科技大学空天科学学院，长沙 410072
2.
太空系统运行与控制全国重点实验室，长沙 410072

基金项目:

国家自然科学基金(12472359,U2441205,62103435)

详细信息

通讯作者:
E-mail：likeboreal@nudt.edu.cn

中图分类号: V448.2；TJ765.3
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- 被引次数: 0
出版历程
- 收稿日期: 2024-05-17
- 录用日期: 2024-08-17
- 网络出版日期: 2025-04-02
- 整期出版日期: 2026-02-28

Multi-to-multi energy optimal task allocation method based on interception capture region

LI Haojian^{1, 2},
LI Kebo^{1, 2
, ,},
LIANG Yangang^{1, 2}

1.
College of Aerospace Science and Engineering，National University of Defense Technology，Changsha 410072，China
2.
State Key Laboratory of Space System Operation and Control，Changsha 410072，China

Funds:

National Natural Science Foundation of China (12472359,U2441205,62103435)

More Information

Corresponding author: E-mail：likeboreal@nudt.edu.cn

摘要

摘要:
针对多拦截器与多目标交战(MME)场景的任务分配问题，从制导的角度入手，提出基于拦截捕获区的“多对多”能量最优目标分配方法。分析三维现实真比例导引(3D-RTPN)拦截任意机动目标的捕获区与最优制导能量消耗；以多拦截器成功拦截机动目标与整体能量消耗最低为优化目标，构建目标分配代价矩阵；基于适应性匈牙利算法(AHA)实现多拦截器的多目标分配(MMA)。通过数值仿真算例，验证了所提方法的有效性。
- 多拦截器多目标交战 /
- 拦截捕获区 /
- 三维真比例导引 /
- 适应性匈牙利算法 /
- 多拦截器多目标分配
Abstract:
Aiming at the assignment problem of multimissile and multitarget engagement (MME) scenario, this paper proposes a multi-to-multi energy optimal task allocation method based on interception capture region from the perspective of guidance. The characteristics of the capture region and optimal energy cost in the scenario of three-dimensional realistic true proportional navigation (3D-RTPN) intercepting arbitrary maneuvering targets are analyzed. The weight matrix is then built with the intention of achieving both the lowest total energy consumption and successful interception. In order to achieve multimissile and multitarget assignment (MMA), the adaptable Hungarian algorithm (AHA) is used. Numerical simulation is used to confirm the MMA strategy's efficacy. The effectiveness of the proposed method is verified by numerical simulation.

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图 1 RTPN三维捕获区

Figure 1. Three-dimensional capture region of RTPN

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图 2 RTPN二维捕获区

Figure 2. Two-dimensional capture region of RTPN

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图 3 拦截仿真结果

Figure 3. Simulation results of interception

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表 1 仿真主要参数

Table 1. Main parameters of simulation

参数	数值
弹头释放位置r_mc/km	[10, 10, 10]^T
子拦截器1位矢r_m1/km	r_mc+[1, 0, 0]^T
子拦截器2位矢r_m2/km	r_mc+[0, 1, 0]^T
子拦截器3位矢r_m3/km	r_mc+[0, 0, 1]^T
子拦截器4位矢r_m4/km	r_mc+[−1, 0, 0]^T
子拦截器5位矢r_m5/km	r_mc+[0, −1, 0]^T
子拦截器6位矢r_m6/km	r_mc+[0, 0, −1]^T
目标1初始时刻位矢r_t1/km	[60, 30, 60]^T
目标2初始时刻位矢r_t2/km	[50, 32, 62]^T
目标3初始时刻位矢r_t3/km	[62, 28, 50]^T
弹头释放时刻速度大小V_m/(m·s⁻¹)	1800
目标1速度大小V_t1/(m·s⁻¹)	2 000
目标2速度大小V_t2/(m·s⁻¹)	2 000
目标3速度大小V_t3/(m·s⁻¹)	2 000
弹头释放时刻速度倾角θ_mc/(°)	45
目标1初始时刻速度倾角θ_t1/(°)	0
目标2初始时刻速度倾角θ_t2/(°)	0
目标3初始时刻速度倾角θ_t3/(°)	0
弹头释放时刻速度方位角ψ_mc/(°)	−45
目标1初始时刻速度方位角ψ_t1/(°)	135
目标2初始时刻速度方位角ψ_t2/(°)	130
目标3初始时刻速度方位角ψ_t3/(°)	140
弹头释放时刻速度矢量v_mc/(m·s⁻¹)	V_m·[cosθ_mccosψ_mc, sinθ_mc, −cosθ_mcsinψ_mc]
子拦截器1速度矢量v_m1/(m·s⁻¹)	v_mc+[100, 0, 0]^T
子拦截器2速度矢量v_m2/(m·s⁻¹)	v_mc+[0, 100, 0]^T
子拦截器3速度矢量v_m3/(m·s⁻¹)	v_mc+[0, 0, 100]^T
子拦截器4速度矢量v_m4/(m·s⁻¹)	v_mc+[−100, 0, 0]^T
子拦截器5速度矢量v_m5/(m·s⁻¹)	v_mc+[0, −100, 0]^T
子拦截器6速度矢量v_m6/(m·s⁻¹)	v_mc+[0, 0, −100]^T

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表 2 不同算法分配结果

Table 2. Allocation results of different algorithms

算法	目标序号
算法	拦截器1	拦截器2	拦截器3	拦截器4	拦截器5	拦截器6
AHA	3	1	2	2	1	3
GA+SQP （无整数约束）	3	1	2	2	1	3
GA（整数约束）	2	1	1	3	2	3

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表 3 不同算法耗时与寻优结果

Table 3. Time consumption and optimization results of different algorithms

算法	求解耗时/s	代价矩阵+ 算法求解耗时/s	速度增量寻优结果/（m·s⁻¹）
AHA	0.0005	0.0077	9465.9
GA+SQP （无整数约束）	9.3168	9.3152	9465.9
GA（整数约束）	64.4299	64.4328	10150.9

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