Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

Unmanned combat air vehicles(UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.

Trajectory online optimization for unmanned combat aerial vehicle using combined strategy

This paper presents a combined strategy to solve the trajectory online optimization problem for unmanned combat aerial vehicle (UCAV). Firstly, as trajectory directly optimizing is quite time costing, an online trajectory functional representation method is proposed. Considering the practical requirement of online trajectory, the 4-order polynomial function is used to represent the trajectory, and which can be determined by two independent parameters with the trajectory terminal conditions; thus, the trajectory online optimization problem is converted into the optimization of the two parameters, which largely lowers the complexity of the optimization problem. Furthermore, the scopes of the two parameters have been assessed into small ranges using the golden section ratio method. Secondly, a multi-population rotation strategy differential evolution approach (MPRDE) is designed to optimize the two parameters; in which, 'current-to-best/1/bin', 'current-to-rand/1/bin' and 'rand/2/bin' strategies with fixed parameter settings are designed, these strategies are rotationally used by three subpopulations. Thirdly, the rolling optimization method is applied to model the online trajectory optimization process. Finally, simulation results demonstrate the efficiency and real-time calculation capability of the designed combined strategy for UCAV trajectory online optimizing under dynamic and complicated environments.

A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory

Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue. © 2014 Chinese Association of Automation.

New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle

Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems.

Key Parameters and Conceptual Configuration of Unmanned Combat Aerial Vehicle Concept

The nature and characteristics of attack unmanned combat aerial vehicle （UCAV） are analyzed. The principles of selecting takeoff thrust-weight ratio and takeoff weight of attack UCAV are presented by analyzing the statistical data of weights for various main combat aircraft. The UCAV airborne weapons are analyzed, followed by the preliminary estimation of the payload weight. Various typical engines are analyzed and one of them is selected. Then the takeoff weight of the UCAV is determined. Based on some basic parameters and assumptions, the qualitative decomposition calculation for takeoff weight is completed. The key factors for obtaining longer endurance of aircraft with small aspect ratio configuration are found to be high lift-drag ratio and internal space. On the basis of the conclusions mentioned above, a highly blended flying-wing plus lifting body concept is proposed. According to this concept, the UCAV configuration is designed and optimized. Finally, the UCAV configuration with small aspect ratio, high lift-drag ratio, and high stealth characteristic is obtained.

基于威胁等效和改进PSO算法的UCAV实时航路规划方法

为解决无人战斗机(unmanned combat aerial vehicle,UCAV)实时航路规划问题,通过对各种威胁等效为雷达威胁,威胁分级和每级分层次的处理方法,得到每个威胁的击毁和击伤作用距离。建立UCAV简易的二维模型,利用其飞行姿态与雷达散射截面积(radar cross section,RCS)之间的关系,得出以探测概率为基础的威胁代价函数。最后运用自适应Meta-Lamarckian学习策略的粒子群优化(particle swarm optimization,PSO)算法对方法进行实时性仿真测试,结果表明此方法的有效性。

利用Radau伪谱法求解UCAV对地攻击轨迹研究

针对UCAV对地攻击轨迹规划问题,提出一种基于Radau伪谱法(RPM)的求解策略。首先,构建了最优控制问题的一般框架,分析了RPM求解最优控制问题的基本原理及实现方式;在充分考虑UCAV的气动力特性、发动机推力特性及大气环境特性的基础上建立了UCAV三自由度(3-DOF)质点模型,并详细分析了UCAV初始和终端位置、速度、姿态约束、飞行性能和战场环境等约束条件,在此基础上构建UCAV对地攻击轨迹规划问题框架;最后,利用RPM将轨迹规划的最优控制问题转化为非线性规划问题并求得最优解。仿真结果表明,该方法能以较高的精度和速度生成满足各种复杂约束要求、连续并且真实可行的最优轨迹。

基于自适应伪谱法的UCAV低可探测攻击轨迹规划研究

研究无人作战飞机(unmanned combat aerial vehicle,UCAV)对地攻击阶段轨迹规划问题。首先,在综合UCAV的气动力特性、发动机推力特性基础上建立UCAV质点模型和动力学模型,并结合UCAV平台初始条件、机动性以及武器投射条件构建约束条件;针对当前轨迹规划中没有考虑雷达散射截面(radar cross section,RCS)随UCAV姿态角改变而动态变化这一缺陷,建立综合考虑动态RCS的威胁概率和攻击时间的目标函数;然后利用可变低阶自适应伪谱法求得攻击轨迹最优解。对时间最短、RCS固定和考虑动态RCS 3种情况进行仿真。结果表明,考虑动态RCS时,UCAV将根据威胁进行轨迹和姿态调整,极大减小了被敌方威胁捕获的概率。该算法能够提供规划轨迹的高精度状态和控制量信息,有利于实现攻击过程的高精度精细规划控制。

基于博弈论的多UCAV对地攻击目标分配

针对多架UCAV对地攻击目标分配问题,首先根据价值属性分别对目标和飞机进行权重分配,在此基础上构建了多UCAV对地攻击目标分配的自主决策函数和协同决策函数,提出了一种基于博弈论的目标分配方法。然后运用该算法进行了实例仿真,仿真结果证明了该算法的可行性和有效性。

多UCAV协同目标攻击决策

针对多无人作战飞机(unmanned combat aerial vehicle,UCAV)攻击多目标,研究了多UCAV协同攻击决策问题。建立了目标毁伤模型、UCAV损耗模型和时间协同模型,并通过加权求和将三者转化为单一目标函数,进而转化为单目标问题进行求解。提出了一种离散微粒群优化(discrete particle swarm optimization,DPSO)算法,在微粒群优化算法框架内重新定义了微粒的位置、速度及相关操作。建立了微粒与实际问题的映射关系,进而使DPSO算法适合于求解多UCAV协同目标攻击决策问题。仿真结果表明,DPSO算法易于实现,能够较好地解决基于时间协同的多UCAV目标攻击决策问题。

基于免疫粒子群算法的多UCAV协同任务分配

任务分配问题是多UCAV协同控制的关键和有效保证。综合考虑问题的多规划指标和多类复杂约束条件,建立了基于多目标整数规划的协同多任务分配模型。通过模拟生物免疫系统的免疫特征和运行机制,并将粒子群优化作为算法的局部搜索算子,设计了一种适用于问题求解的免疫粒子群算法,使算法同时具有人工免疫算法种群多样性好、粒子群优化局部搜索能力和进化方向性强等特点。仿真实验表明该方法具有良好的优化效果和时间特性,可较好地解决多UCAV协同任务分配问题。

复杂不确定环境下UCAV自主攻击轨迹优化设计

针对不确定环境下无人作战飞机(UCAV)自主攻击关键问题,提出一种滚动伪谱法求解最优攻击轨迹的策略。首先设计出UCAV自主攻击过程总体框架,随后建立了UCAV三自由度质点模型,基于动态RCS构建统一威胁模型,采用威胁等效的方法快速处理突发威胁,以滚动优化策略解决大规模不确定环境下UCAV视野域范围有限、不易处理突发威胁以及因规划空间增大而产生的维数灾难等问题,以预测规划解决滚动方法带来的局部最优问题以及可能面临的有限时间内攻击轨迹解算困难而无解的问题。数字仿真结果表明,该算法能以较高的速度和精度在复杂不确定环境中快速规划出满足约束要求的最优对地攻击轨迹,且精度高、跟踪效果好,从而证明了该算法的有效性。

基于ABFCM模型框架的UCAV自主攻击决策

为发挥无人作战飞机(unmanned combat aerial vehicle,UCAV)的决策自主性、减轻UCAV操作员的任务负担,基于智能体模糊认知图(agent-based fuzzy cognitive map,ABFCM)模型框架,设计了包含信息处理、决策推理和人机交互的三层次UCAV自主攻击决策系统ABFCM模型。该模型将UCAV自主攻击决策内容合理地分解在不同层次和功能的Agent中,在分别建立各Agent因果推理模型的基础上,实现了合理有序地组织整个决策推理行为。通过仿真分析,验证了所建模型的合理性和可行性,同时也表明该自主攻击决策模型将有助于UCAV适应未来复杂的战场环境,提升监督控制模式下的自主作战能力。

UCAV协同攻击多目标的任务分配技术研究

为解决单目标函数构建的任务分配模型不能给火控决策者提供更多有用信息的问题,将无人机(UCAV:Unmanned Combat Aerial Vehicle)损耗代价和目标毁伤价值作为UCAV协同攻击任务分配的两个目标函数,对其进行多目标优化,建立新型任务分配模型。在此基础上,采用一种改进带精英策略的快速非支配排序遗传算法(NSGA-II:Nondominated Sorting Genetic Algorithm II)进行求解,得到多目标协同攻击任务分配的Pareto最优解集,然后根据决策者的偏好选取最佳的任务分配方案。最后通过仿真算例,验证了该算法的收敛性及有效性。

基于多平台实现的UCAV攻击编队自主协作系统设计

为了实现无人战斗机的编队攻击,在深入分析编队协作复杂性的基础上,采用模块化设计,根据高可靠性原则,在V isual C++6.0环境中,提出了一种可以在不同平台中实现功能的UCAV攻击编队自主协作系统的设计方法,并对各个子系统的组成和功能进行了探讨,给出了该系统在整个作战过程中的工作流程,最后对正常攻击、单个任务失败、基于不同平台的系统实现3种情况进行了仿真分析。结果表明,该系统能够在不同的平台中可靠地实现UCAV编队自主协作攻击过程。

基于灰色区间关联的UCAV自主决策方法

为解决信息不完备条件下的无人作战飞机(UCAV,Unmanned Combat Air Vehicle)战术决策问题,提出一种基于灰色区间关联的UCAV自主战术决策方法.依照作战任务要求选取决策要素,建立UCAV决策推理的规则库.构建不完备信息模型,并基于灰色区间关联理论给出UCAV战术决策模型;设计冲突消解算法,有效解决不完备信息导致的推理失效问题.仿真实例模拟了决策过程,验证了该方法在解决UCAV战术决策问题上的可行性和在化解规则匹配冲突方面的有效性.仿真结果表明,该方法能够应对决策要素不确定性较大的情况,并给出合理的战术行为推理结果.

改进粒子群算法在UCAV航路规划中的应用

针对在无人作战飞机(UCAV)航路规划中存在的计算复杂和收敛性等问题,该文利用标准粒子群算法原理,在算法搜索过程中引入变异算子,克服了标准算法易陷入局部极值点的不足。利用一组正弦波曲线来构造一个粒子,通过对正弦波个数和幅值的限制,使该方法得到的飞行航路严格经过起始点和目标点,而且满足UCAV的机动性能要求。仿真结果表明该方法简便可行,粒子能较快地收敛于全局最佳航路。

基于记忆辅助动态UMDA的UCAV编队动态目标分配

针对无人作战飞机编队对地攻击过程中的动态目标分配问题,建立了针对联合目标的武器目标分配(weapon target assignment,WTA)模型,反映攻击方攻击意图及对目标内在关系的理解。提出基于记忆辅助的动态单变量分布估计算法(memory enhanced dynamic univariate marginal distribution algorithm,MDUMDA)对问题进行动态寻优,利用概率模型对动态寻优过程中的历史信息加以记忆和利用。仿真结果表明,所建立的WTA模型是合理的,MDUMDA能够有效求解动态WTA问题,其性能明显优于随机迁移算法。

基于案例和MAX-MIN云推理的UCAV双层战术决策

针对无人作战飞机空战过程中的战术决策问题,借鉴认知心理学双重加工理论,提出了一种案例推理与MAX-MIN云推理机制相结合的双层战术决策方案。首先,将飞行员作战经验案例化以构建案例库,将问题情境和案例库中的源案例进行相似度匹配以实现第一层基于案例推理的启发式决策,若是低于相似度阈值则转向第二层基于MAX-MIN云推理的分析决策,用前件云发生器对空战特征属性云化以形成属性云滴;然后,结合定性规则,采用MAX-MIN云推理算法得到规则云滴,进而得出战术决策方案。仿真结果表明,提出的双层战术决策可有效实现无人作战飞机空战战术决策,决策时间符合空战实时性要求。

基于模糊决策树的UCAV雷达模式管理算法

无人作战飞机作战态势变化多样,地面指挥人员对机载雷达操控困难。针对不同作战态势下雷达工作模式选择需求不同,为了提高雷达的探测精度,提出了一种基于模糊决策树的无人作战飞机雷达模式管理算法。算法利用专家知识,以敌方目标和模式选择结果的经验数据为训练样本,以作战过程中的实时目标为测试数据。通过样本的模糊信息生成树的属性节点和分支,对测试数据进行推理分类,分类结果以信任度表示,为实时的雷达模式管理方式,并进行空战想定下的仿真试验。结果表明,各模式信任度的取值变化与空战中雷达各模式的需求程度变化相吻合,提高雷达探测精确性,实现无人作战飞机雷达智能管理的要求。