Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and miss...Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.展开更多
Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzz...Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.展开更多
A weapon target assignment(WTA)model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon a...A weapon target assignment(WTA)model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted.The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result.Ant colony algorithm has been successfully used in many fields,especially in combination optimization.The ant colony algorithm for this WTA problem is described by analyzing path selection,pheromone update,and tabu table update.The effectiveness of the model and the algorithm is demonstrated with an example.展开更多
With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive o...With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.展开更多
The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present ...The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic WTA are discussed.展开更多
The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In t...The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.展开更多
Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model base...Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.展开更多
Weapon Target Assignment is not only an important issue to use firepower, but also an important operational decision-making problem. As new intelligent algorithms, Genetic algorithm and ant colony algorithm are applie...Weapon Target Assignment is not only an important issue to use firepower, but also an important operational decision-making problem. As new intelligent algorithms, Genetic algorithm and ant colony algorithm are applied to solve Weapons-Target Assignment Problem. This paper introduces the Weapon-Target Assignment (WTA) and the mathematical model, and proposes ACGA algorithm which is the integration of genetic algorithm and ant colony algorithm then use ACGA algorithm to solve the Weapon-Target Assignment Problem. Calculations show that: when ACGA algorithm is used to solve Weapon – Target Assignment Problem, it has fast convergence and high accuracy.展开更多
A new file assignment strategy of parallel I/O, which is named heuristic file sorted assignment algorithm was proposed on cluster computing system. Based on the load balancing, it assigns the files to the same disk ac...A new file assignment strategy of parallel I/O, which is named heuristic file sorted assignment algorithm was proposed on cluster computing system. Based on the load balancing, it assigns the files to the same disk according to the similar service time. Firstly, the files were sorted and stored at the set I in descending order in terms of their service time, then one disk of cluster node was selected randomly when the files were to be assigned, and at last the continuous files were taken orderly from the set I to the disk until the disk reached its load maximum. The experimental results show that the new strategy improves the performance by 20.2% when the load of the system is light and by 31.6% when the load is heavy. And the higher the data access rate, the more evident the improvement of the performance obtained by the heuristic file sorted assignment algorithm.展开更多
在多无人机协同突防过程中,针对无人机需完成目标分配与探干侦动作选择的任务决策存在模型构建难,对模型求解方法复杂度高的问题,提出一种蒙特卡罗树搜索(Monte Carlo tree search,MCTS)的改进方法实现多无人机多任务联合决策。首先,考...在多无人机协同突防过程中,针对无人机需完成目标分配与探干侦动作选择的任务决策存在模型构建难,对模型求解方法复杂度高的问题,提出一种蒙特卡罗树搜索(Monte Carlo tree search,MCTS)的改进方法实现多无人机多任务联合决策。首先,考虑无人机与雷达对抗中的角度、距离等态势要素,以及当前无人机动作执行成功概率和雷达状态有效概率,构建多无人机目标分配与探干侦动作统一决策数学模型。其次,提出搜索次数自适应调整的改进MCTS算法对模型求解,实现大规模解空间在线快速寻优。仿真结果表明,改进算法使多雷达系统对无人机的威胁程度下降约16.8%,相比多臂赌博机算法效果提升约5.08%,决策时间约0.23 s,比传统MCTS缩短约45.7%,有助于提升无人机战场生存率。展开更多
针对区域防空反导作战中各要素复杂耦合所导致的战场态势快速演变、来袭目标数量动态变化等难题,提出一种基于可动态扩展且带时空推理的QMIX(QMIX with Dynamic extension and Spatiotemporal reasoning, QMIX-DS)的火力分配方法,以火...针对区域防空反导作战中各要素复杂耦合所导致的战场态势快速演变、来袭目标数量动态变化等难题,提出一种基于可动态扩展且带时空推理的QMIX(QMIX with Dynamic extension and Spatiotemporal reasoning, QMIX-DS)的火力分配方法,以火力单元作为智能体构建决策网络,生成火力分配策略。核心改进为:为每个智能体的决策网络设计可动态扩展特征编码模块,自适应处理数量变化的来袭目标,并引入对比学习突出目标类别属性,形成差异化特征表征;构建两层多头自注意力机制捕捉不同类别目标间的动态时空依赖关系,快速推理任务过程中的态势演变,优化火力分配策略。基于墨子平台不同规模的仿真结果表明,所提出的火力分配方法能够在动态变化的战场条件下生成有效的防空反导策略,与基线算法及其他主流算法相比,所提QMIX-DS算法在目标拦截率、阵地存活率、导弹消耗数量等指标上均体现出了优势,并在不同场景中展现出较高的扩展性和泛化性。展开更多
文摘Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.
基金supported by The National Natural Science Foundation of China under Grant Nos.61402517, 61573375The Foundation of State Key Laboratory of Astronautic Dynamics of China under Grant No. 2016ADL-DW0302+2 种基金The Postdoctoral Science Foundation of China under Grant Nos. 2013M542331, 2015M572778The Natural Science Foundation of Shaanxi Province of China under Grant No. 2013JQ8035The Aviation Science Foundation of China under Grant No. 20151996015
文摘Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.
文摘A weapon target assignment(WTA)model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted.The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result.Ant colony algorithm has been successfully used in many fields,especially in combination optimization.The ant colony algorithm for this WTA problem is described by analyzing path selection,pheromone update,and tabu table update.The effectiveness of the model and the algorithm is demonstrated with an example.
基金supported by the National Natural Science Foundation of China(71771216).
文摘With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.
基金This project was supported by the National Defense Pre-Research Foundation of China
文摘The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic WTA are discussed.
基金National Natural Science Foundation of China(Grant No.62001506)to provide fund for conducting experiments。
文摘The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.
文摘Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.
文摘Weapon Target Assignment is not only an important issue to use firepower, but also an important operational decision-making problem. As new intelligent algorithms, Genetic algorithm and ant colony algorithm are applied to solve Weapons-Target Assignment Problem. This paper introduces the Weapon-Target Assignment (WTA) and the mathematical model, and proposes ACGA algorithm which is the integration of genetic algorithm and ant colony algorithm then use ACGA algorithm to solve the Weapon-Target Assignment Problem. Calculations show that: when ACGA algorithm is used to solve Weapon – Target Assignment Problem, it has fast convergence and high accuracy.
文摘A new file assignment strategy of parallel I/O, which is named heuristic file sorted assignment algorithm was proposed on cluster computing system. Based on the load balancing, it assigns the files to the same disk according to the similar service time. Firstly, the files were sorted and stored at the set I in descending order in terms of their service time, then one disk of cluster node was selected randomly when the files were to be assigned, and at last the continuous files were taken orderly from the set I to the disk until the disk reached its load maximum. The experimental results show that the new strategy improves the performance by 20.2% when the load of the system is light and by 31.6% when the load is heavy. And the higher the data access rate, the more evident the improvement of the performance obtained by the heuristic file sorted assignment algorithm.
文摘针对遥感图像中大纵横比目标因正样本不足而出现的学习不充分问题,提出一种基于形状自适应标签分配的遥感有向目标检测网络(shape-adaptive label assignment for oriented object detection network,SALANet)。首先,引入纵横比敏感系数建立目标几何特征与正样本数量的动态映射关系,缓解传统方法中固定分配规则引发的样本分布不平衡问题;其次,设计自适应标签分配策略,通过对交并比(intersection over union,IoU)进行排名实现高质量正样本选择;最后,提出中心轴先验,将圆形中心先验区扩展为目标中心轴的矩形区域,增强大纵横比目标的几何特征表征能力。在DOTAv1.0和HRSC2016数据集上的对比实验表明,SALANet分别取得0.777 1和0.932 3的平均精度均值(mean average precision,mAP),较基线方法RoI Transformer分别提升8.15%和2.87%。
文摘在多无人机协同突防过程中,针对无人机需完成目标分配与探干侦动作选择的任务决策存在模型构建难,对模型求解方法复杂度高的问题,提出一种蒙特卡罗树搜索(Monte Carlo tree search,MCTS)的改进方法实现多无人机多任务联合决策。首先,考虑无人机与雷达对抗中的角度、距离等态势要素,以及当前无人机动作执行成功概率和雷达状态有效概率,构建多无人机目标分配与探干侦动作统一决策数学模型。其次,提出搜索次数自适应调整的改进MCTS算法对模型求解,实现大规模解空间在线快速寻优。仿真结果表明,改进算法使多雷达系统对无人机的威胁程度下降约16.8%,相比多臂赌博机算法效果提升约5.08%,决策时间约0.23 s,比传统MCTS缩短约45.7%,有助于提升无人机战场生存率。
文摘针对区域防空反导作战中各要素复杂耦合所导致的战场态势快速演变、来袭目标数量动态变化等难题,提出一种基于可动态扩展且带时空推理的QMIX(QMIX with Dynamic extension and Spatiotemporal reasoning, QMIX-DS)的火力分配方法,以火力单元作为智能体构建决策网络,生成火力分配策略。核心改进为:为每个智能体的决策网络设计可动态扩展特征编码模块,自适应处理数量变化的来袭目标,并引入对比学习突出目标类别属性,形成差异化特征表征;构建两层多头自注意力机制捕捉不同类别目标间的动态时空依赖关系,快速推理任务过程中的态势演变,优化火力分配策略。基于墨子平台不同规模的仿真结果表明,所提出的火力分配方法能够在动态变化的战场条件下生成有效的防空反导策略,与基线算法及其他主流算法相比,所提QMIX-DS算法在目标拦截率、阵地存活率、导弹消耗数量等指标上均体现出了优势,并在不同场景中展现出较高的扩展性和泛化性。
