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.

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.

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.

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。

基于有限忍耐度鸽群优化的无人机近距空战机动决策

由于对抗双方态势的快速变化,无人机近距空战机动自主决策困难且复杂,是空中对抗的一个难点。对此,提出一种基于有限忍耐度鸽群优化(FTPIO)算法的无人机近距空战机动决策方法。该方法主要包括基于机动动作库的对手行动预测和基于FTPIO算法的机动控制量和执行时间优化求解两个部分。为提升基本鸽群优化(PIO)算法的全局探索能力,引入有限忍耐度策略,在鸽子个体几次迭代中没有找到更优解时对其属性进行一次重置,避免陷入局部最优陷阱。该方法采用的优化变量是无人机运动模型控制变量的增量,打破了机动库的限制。通过和极小极大方法、基本PIO算法和粒子群优化(PSO)算法的仿真对抗测试结果表明,所提出的机动决策方法能够在近距空战中有效击败对手,产生更为灵活的欺骗性机动行为。

装备智能保障技术研究

体系对抗中通过创新地面装备实现体系赋能、体系增强和体系协同,是地面总体专业面临的重大难题。以典型装备系统为研究对象,分析体系对抗对该武器系统的能力需求,结合武器系统的任务模式、任务流程和系统组成,梳理保障能力需求清单;以提升伴随保障支持管理能力、改善伴随保障资源配置为目标,梳理智能技术应用于机动伴随保障和增强地面装备效能的技术途径,形成装备智能保障技术方案。基于上述研究,完成信息化、智能化保障车方案概要设计,对其进行机动伴随保障能力评估。

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.

典型作战流程和分级环境下发射车生存能力改进ADC评估模型

为了综合评估发射车在作战中的生存能力,区分参数-性能-能力-效能四个层次建立武器装备评估空间,构建对象、内容和层次三个维度的评估框架,划分五类自然环境、三级威胁环境和三种作战状态,将外界环境引入评估过程,用被发现概率和损毁概率表征发射车隐蔽伪装能力和抗毁防护能力,建立发射车作战状态转移概率矩阵,提出典型作战流程和分级环境下的发射车生存能力改进可靠性、可信性、可用性综合评估模型。通过仿真分析,在不同环境和作战状态下各型发射车生存能力差异明显,模型能在性能空间、环境空间、作战流程空间多个维度动态评估发射车的生存能力,为发射车性能设计、战场运用提出量化和优化建议。

俄乌冲突中无人艇海上作战模式的新突破

近年来,随着科技的迅猛发展,无人艇技术在军事领域的应用逐渐成为国际军事竞争的焦点之一。在俄乌冲突中,无人艇海上作战模式展现出了新的突破,为未来海上作战提供了新的思路和可能性。本文通过对俄乌战争中无人艇海上作战的实际案例分析,探讨了其在战争中的应用价值、技术特点以及未来发展趋势,旨在为相关领域的研究和实践提供参考和启示。

履带式装甲战斗车辆传动装置服役性能评价指标体系构建

为实现对履带式装甲战斗车辆传动装置服役性能的评价,根据指标体系构建原则,运用扎根理论从现有履带式装甲战斗车辆传动装置构建的多种性能指标体系资料入手,初步构建履带式装甲战斗车辆传动装置部件服役性能评价指标体系,同时采用系统分析方法从宏观角度初步构建履带式装甲战斗车辆传动装置整体服役性能评价指标体系,最后从指标相关性与显著性两个角度对指标体系进行优化。最终建立了完善的履带式装甲战斗车辆传动装置服役性能评价指标体系。

基于GA-BP神经网络的多无人艇协同作战效能评估

在现代化海战中,多无人艇协同作战作为新的作战形式,对其作战效能进行科学准确的评估十分重要。针对多无人艇系统协同作战的特点,结合ADC方法和OODA决策链建立协同作战效能评估指标体系。考虑传统评估方法具有过于依赖专家经验的缺点,引入BP神经网络构建多无人艇协同作战评估模型,利用遗传算法(GA)对神经网络进行全局优化并对模型进行仿真验证。结果表明,该模型可有效地对多无人艇系统协同作战效能进行评估。

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.

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.

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.

轮腿式无人战车越障性能分析

针对非结构化复杂路面环境下无人战车在执行军事运输、抢险救灾等任务时通过性不足的问题,提出了一种轮腿式全液压驱动的8轮无人战车,该战车通过摆臂的协同控制调整其运行姿态,可适应不同的复杂地形。越障性能是衡量无人战车通过性的关键要素,建立无人战车的越障动力学模型和姿态规划模型,通过求取轮腿式无人战车不同越障高度与摆臂摆角的关系式,得到无人战车典型垂直墙障碍的越障性能。在理论分析的基础上,利用QT、Simulink、ADAMS建立综合仿真平台,进行了仿真验证。研究表明:轮腿式全液压驱动的8轮无人战车可完成高度为轮胎直径1.176倍的垂直墙越障,具有良好的地形适应能力,为8轮无人战车的实验测试提供了理论参考。

国外无人船集群运用特点分析及其应对启示

无人船(USV)集群作为未来海上军事运用的重要组成力量,是各国海上无人装备的重要发展方向。首先,通过梳理总结当前国外无人船集群发展战略规划与运用现状,对无人船集群作战关键技术进行归纳分析,从集群自组网通信、战场态势感知、集群协同任务分配等3个方面剖析无人船集群相关技术的研究进展;然后,对无人船集群在军事上运用的典型样式和主要效益进行分析及研判;最后,总结分析当前应对无人船集群的难点,并对无人船集群应对的技术发展予以展望。

有人/无人机协同系统及关键技术综述

有人/无人机协同系统是在无人机系统基础上发展起来的一种新型协同作战系统.随着任务复杂度的不断加深,仅凭无人机不能满足各类任务需求,有人机承担的指挥、控制和决策功能的重要性逐步增强,是系统任务执行效能得以提升的关键要素.凭借无人机强大的感知、计算、通讯能力以及机载飞行员的高级智慧和经验,有人机和无人机可以实现协同编队,完成各种复杂任务.深入分析了有人/无人机协同系统的架构及组成,总结了目前的发展状况,提炼和归纳了系统的关键技术.最后对系统未来的发展方向进行了展望.

基于AM-SAC的无人机自主空战决策

针对现代空战中的无人机自主决策问题,将注意力机制(AM)与深度强化学习中的非确定性策略算法Soft Actor Critic(SAC)相结合,提出一种基于AM-SAC算法的机动决策算法。在1V1的作战背景下建立无人机3自由度运动模型和无人机近距空战模型,并利用敌我之间相对距离和相对方位角构建导弹攻击区模型。将AM引入SAC算法,构造权重网络,从而实现训练过程中奖励权重的动态调整并设计仿真实验。通过与SAC算法的对比以及在多个不同初始态势环境下的测试,验证了基于AM-SAC算法的机动决策算法具有更高的收敛速度和机动稳定性,在空战中有更好的表现,且适用于多种不同的作战场景。

高超声速飞行器数据链关键技术分析及展望

数据链技术随着武器装备、作战理念及通信技术的发展而不断更新换代,在“无链不成导”“无导不成战”的空中作战趋势下占有重要地位。高超声速飞行器独有的高速、跨域等特点使得其成为空战体系中的制高点。数据链技术为高超声速飞行器作战效能最大化提供技术保障,反过来高超声速飞行器的发展也拓宽了数据链技术的边界,在不同的作战任务中对数据链指标参数的要求(传输速率、吞吐量、丢包率、延时等)不尽相同。近年来,5G、物联网、云计算、人工智能等新兴技术的飞速发展加速了战场态势感知形式从物理域、信息域向认知域的变革。本文简要回顾了美军数据链技术的发展特点,详细分析了高超声速飞行器由大包线飞行特性引发的数据链技术设计挑战,探讨了高超声速飞行器数据链的顶层需求及关键技术。