Air-Combat Decision-Making for UAVs Cooperatively Attacking Multiple Targets

中文摘要：针对超视距空战中多架无人机对空中的多个敌对目标进行协同攻击的决策问题进行了研究。首先，对空战威胁态势进行了分析，基于对各攻击目标至少分配一枚导弹的原则，将协同多目标攻击决策问题转化为导弹目标攻击配对的优化问题并建立其攻击效能评估模型。然后，提出了一种模拟退火遗传算法用于该决策问题的寻优。最后，通过所得最佳导弹目标分配个体求得最终协同攻击决策方案。仿真结果表明所提出的算法能有效地求解协同多目标攻击决策问题，其对最优解的搜索效率明显优于单一的遗传算法。 英文摘要：Abstract: Considering a Beyond Visual Range (BVR) air combat scenario with a group of UAVs versus multiple hostile airborne targets, the decision-making problem for Cooperative Attack on Multiple Targets (CAMT) was investigated. First, the air combat threat situation was analyzed. Based on the principle of each target to be attacked at least being assigned one missile, the decision-making for CAMT was converted into a Missile-Target Assignment (MTA) optimization problem with the establishment of the attack effectiveness evaluation model. Then, a Simulated Annealing Genetic Algorithm (SAGA) was proposed to find out the optimal solution to the MTA problem. Finally, the final decision-making solution to the CAMT was derived from the obtained best missile-target assignment individual. Simulation results show that the proposed method is more effective than Genetic Algorithm (GA) to deal with the decision-making problem for CAMT.基金项目：国家自然科学基金(60604009）；航空科学基金(2006ZC51039)；厦门大学985 二期信息创新平台项

Research on Air Combat Decision-making for Cooperative Multiple Target Attack Using Heuristic Ant Colony Algorithm

协同多目标攻击空战决策是现代战机在超视距条件下进行协同空战的关键技术之一。它是寻求一个优化分配方案,将目标分配给各友机,力求使攻击效果最优。本文在对协同多目标攻击战术进行深入分析的基础上,提出了一种用于空战决策的启发式蚁群算法,该算法通过求解友机导弹对目标的最优分配来确定空战决策方案。仿真实验表明所提出的启发式蚁群算法对最优解的搜索效率明显优于基本蚁群算法,是一种求解协同多目标攻击空战决策问题的有效算法。The air combat Decision-Making(DM)for Cooperative Multiple Target Attack(CMTA)is one of the key techniques for modern fighters performing cooperative air combat under the Beyond Visual Range (BVR)condition.It is to find a proper assignment of the friendly fighters to the hostile fighters to achieve the optimal attack effect.In this paper,based on the deep analysis of the CMTA tactics,a Heuristic Ant Colony Algorithm(HACA)is proposed to solve the DM problem.The HACA obtains the DM solution by searching for the optimal assignment of the missiles of the friendly fighters to the hostile fighters.Simulation results show that the search efficiency of the proposed algorithm is obviously superior to that of basic Ant Colony Al- gorithm(ACA).It is an effective algorithm to deal with the DM problem for CMTA in air combat.国家自然科学基金(60604009);; 厦门大学985二期信息创新平台基金;; 福建省教委科技基金(JA05290

Measurement of integrated luminosity of data collected at 3.773 GeV by BESIII from 2021 to 2024

We present a measurement of the integrated luminosity e+e- of collision data collected by the BESIII detector at the BEPCII collider at a center-of-mass energy of Ecm = 3.773 GeV. The integrated luminosities of the datasets taken from December 2021 to June 2022, from November 2022 to June 2023, and from October 2023 to February 2024 were determined to be 4.995±0.019 fb-1, 8.157±0.031 fb-1, and 4.191±0.016 fb-1, respectively, by analyzing large angle Bhabha scattering events. The uncertainties are dominated by systematic effects, and the statistical uncertainties are negligible. Our results provide essential input for future analyses and precision measurements

Determination of the number of ψ(3686) events taken at BESIII

The number of ψ(3686) events collected by the BESIII detector during the 2021 run period is determined to be (2259.3±11.1)×106 by counting inclusive ψ(3686) hadronic events. The uncertainty is systematic and the statistical uncertainty is negligible. Meanwhile, the numbers of ψ(3686) events collected during the 2009 and 2012 run periods are updated to be (107.7±0.6)×106 and (345.4±2.6)×106, respectively. Both numbers are consistent with the previous measurements within one standard deviation. The total number of ψ(3686) events in the three data samples is (2712.4±14.3)×10^