Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.

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.

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.

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning(DRL) algorithm: the multistep double deep Q-network(MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.

Optimal confrontation position selecting games model and its application to one-on-one air combat

In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.

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.

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.

A Multi-UCAV cooperative occupation method based on weapon engagement zones for beyond-visual-range air combat

Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.

Computational engineering analysis of external geometrical modifications on MQ-1 unmanned combat aerial vehicle

This paper focuses on the effects of external geometrical modifications on the aerodynamic characteristics of the MQ-1 predator Unmanned Combat Aerial Vehicle(UCAV)using computational fluid dynamics.The investigations are performed for 16 flight conditions at an altitude of7.6 km and at a constant speed of 56.32 m/s.Two models are analysed,namely the baseline model and the model with external geometrical modifications installed on it.Both the models are investigated for various angles of attack from-4°to 16°,angles of bank from 0°to 6°and angles of yaw from 0°to 4°.Due to the unavailability of any experimental(wind tunnel or flight test)data for this UCAV in the literature,a thorough verification of calculations process is presented to demonstrate confidence level in the numerical simulations.The analysis quantifies the loss of lift and increase in drag for the modified version of the MQ-1 predator UCAV along with the identification of stall conditions.Local improvement(in drag)of up to 96%has been obtained by relocating external modifications,whereas global drag force reduction of roughly 0.5%is observed.The effects of external geometrical modifications on the control surfaces indicate the blanking phenomenon and reduction in forces on the control surfaces that can reduce the aerodynamic performance of the UCAV.

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.

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.

基于分组教与学的无人战斗机自适应路径规划

针对无人战斗机(unmanned combat air vehicle,UCAV)处于存在威胁区域的战场中路径规划问题,提出一种基于分组教与学算法的UCAV自适应路径规划方法。通过分析UCAV路径评价指标,提出一种自适应的UCAV路径评价模型,根据作战环境规划出距离短、威胁小的任务路径。针对教与学算法寻优精度低、耗时长的问题,提出一种分组教与学算法,引入动态分组和高斯分布扰动策略,提高算法寻优性能。通过仿真实验,该方案求解的最优路径更短且安全。

基于图卷积深度强化学习的协同空战机动决策方法

针对多无人机智能协同空战对抗决策问题,提出了一种基于长短期记忆与竞争图卷积深度强化学习的多机协同空战机动对抗决策方法.首先,对多机协同空战对抗问题进行描述;其次,在竞争Q网络中,引入长短期记忆网络用于处理带有强时序相关性的空战信息,接着,搭建图卷积网络作为多机之间的通信基础,提出基于长短期记忆与竞争图卷积深度强化学习算法的协同空战训练框架,并对协同空战决策训练算法进行了设计.二对一空战仿真结果验证了本文所提出的协同智能对抗决策方法的有效性,其具有决策速度快、学习过程稳定的特点以及适应空战环境快速变化下的协同策略学习能力.

多UCAV协同控制中的任务分配模型及算法

任务分配是多UCAV协同控制的核心和有效保证。分析了影响目标价值毁伤、UCAV损耗、任务消耗时间等三项关键战技指标的因素,综合考虑实战中多UCAV同时攻击同一目标和使用软杀伤武器这两种典型情况对UCAV执行任务的影响,建立了针对攻击任务的多UCAV协同任务分配模型,并应用粒子群算法求解。仿真结果验证了模型的合理性和算法的有效性。

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。

面向协同任务的多UCAV分布式任务分配与协调技术

对多架无人作战飞机(Unmanned combat aerial vehicle,UCAV)分布式控制中的任务分配与任务协调问题开展研究．采用合同网实现任务执行过程中的任务分配．通过对合同网进行扩展,使UCAV能够在招标和竞标的同时处理任务间的时间约束．设计了一种新的部分全局规划协商机制,能够更有效地对执行相关任务的UCAV的任务计划进行协调．通过Petri网建模方法,将两种协商机制模型化为Petri网并分析了协商过程的正确性与协商结果的可行性．仿真实验验证了方法的有效性．

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。

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。

多UCAV协同中基于协商的分布式任务分配研究

针对多无人作战飞机(UCAV)分布式协同任务分配问题展开研究。在对多UCAV任务分配问题进行分析的基础上,提出了基于市场协调机制的多UCAV分布式协同任务分配体系结构,设计了能够支持不同自主能力UCAV的任务控制模型,各UCAV在分布式计算的基础上进行相互协商实现动态任务分配。通过综合采用买卖合同、交换合同和聚类合同三种协调机制,实现了多UCAV协同作战中的分布式任务分配。仿真实验结果表明,基于协商的分布式任务分配方法能够快速有效地实现对态势变化的反应,对于解决作战过程中的动态任务分配具有突出优势。

基于FBNs的有人机/UCAV编队对地攻击威胁评估

信息化环境下的编队对地攻击过程中,所获取的地面兵力威胁信息具有高度不确定性。如何采用高效的不确定性信息推理方法快速准确地完成地面兵力对空中飞行编队的威胁评估建模成为一个亟待解决的问题。提出基于模糊贝叶斯网络的有人机/无人战斗机编队对地威胁评估建模方法,综合模糊逻辑与贝叶斯网络的优势,对战场的随机、模糊威胁信息进行综合处理。仿真结果表明,模糊贝叶斯网络不仅能够有效处理战场不确定信息,而且能够综合指挥员的主观判断,适合于不确定性战场环境下的威胁评估推理。