The weapon target assignment (WTA) is a classical problem of defense-related applications which is proved to be a NP-complete problem. In this paper, a practical and available dynamic weapon target assignment (DWTA) formulation is given which incorporates two meaningful and conflicting objectives, that is, minimizing weapon costs and maximizing combat benefits. As we know, heuristic methods have some shortcomings such as slow convergence speed and local optimum in solving the nonlinear integer optimization problem. To this end, a novel DWTA algorithm based on cross-entropy (CE) method is introduced, where the resources requirement condition for targets is taken into consideration. The CE method associates an estimation problem with the DWTA optimization problem, and then, the estimation problem is transformed into a convex optimization problem. The Karush–Kuhn–Tucker conditions are applied to solve the convex optimization problem, and the iteration formulas to find the optimal solution are deducted. Furthermore, in order to verify the performance of CE method in dealing with the DWTA problem, several simulations in different combat scenarios are implemented. The results reveal that, compared with the benchmark heuristic and Monte-Carlo (MC) methods, there are some notable advantages in solving the DWTA problem based on CE method with regard to the solution quality and time consumption.

中文翻译：

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基于交叉熵的动态武器目标分配研究

武器目标分配（WTA）是国防相关应用的经典问题，被证明是NP完全问题。在本文中，给出了一种实用且可用的动态武器目标分配（DWTA）公式，其中包含两个有意义且相互矛盾的目标，即最大限度地减少武器成本和最大化作战收益。众所周知，启发式方法在解决非线性整数优化问题上存在收敛速度慢，局部最优等缺点。为此，提出了一种基于交叉熵（CE）的DWTA算法，该算法考虑了目标的资源需求条件。CE方法将估计问题与DWTA优化问题相关联，然后将估计问题转换为凸优化问题。应用Karush–Kuhn–Tucker条件解决凸优化问题，并推导找到最佳解的迭代公式。此外，为了验证CE方法在处理DWTA问题上的性能，在不同的战斗场景中进行了几种模拟。结果表明，与基准启发式和蒙特卡洛（MC）方法相比，基于CE方法解决DWTA问题在解决方案质量和时间消耗方面均具有明显优势。在不同的战斗场景中进行了几种模拟。结果表明，与基准启发式和蒙特卡洛（MC）方法相比，基于CE方法解决DWTA问题在解决方案质量和时间消耗方面均具有明显优势。在不同的战斗场景中进行了几种模拟。结果表明，与基准启发式和蒙特卡洛（MC）方法相比，基于CE方法解决DWTA问题在解决方案质量和时间消耗方面均具有明显优势。