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.

Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

In this paper, we propose a joint waveform selection and power allocation(JWSPA) strategy based on chance-constraint programming(CCP) for manned/unmanned aerial vehicle hybrid swarm(M/UAVHS) tracking a single target. Accordingly,the low probability of intercept(LPI) performance of system can be improved by collaboratively optimizing transmit power and waveform. For target radar cross section(RCS) prediction, we design a random RCS prediction model based on electromagnetic simulation(ES) of target. For waveform selection, we build a waveform library to adaptively manage the frequency modulation slope and pulse width of radar waveform. For power allocation,the CCP is employed to balance tracking accuracy and power resource. The Bayesian Cramér-Rao lower bound(BCRLB) is adopted as a criterion to measure target tracking accuracy. The hybrid intelli gent algorithms, in which the stochastic simulation is integrated into the genetic algorithm(GA), are used to solve the stochastic optimization problem. Simulation results demonstrate that the proposed JWSPA strategy can save more transmit power than the traditional fixed waveform scheme under the same target tracking accuracy.

Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modifed genetic algorithm with multi-type genes

The task assignment problem of multiple heterogeneous unmanned aerial vehicles （UAVs）, concerned with cooperative decision making and control, is studied in this paper. The heterogeneous vehicles have different operational capabilities and kinematic constraints, and carry limited resources （e.g., weapons） onboard. They are designated to perform multiple consecutive tasks cooperatively on multiple ground targets. The problem becomes much more complicated because of these terms of heterogeneity. In order to tackle the challenge, we modify the former genetic algorithm with multi-type genes to stochastically search a best solution. Genes of chromo- somes are different, and they are assorted into several types according to the tasks that must be performed on targets. Different types of genes are processed specifically in the improved genetic operators including initialization, crossover, and mutation. We also present a mirror representation of vehicles to deal with the limited resource constraint. Feasible chromosomes that vehicles could perform tasks using their limited resources under the assignment are created and evolved by genetic operators. The effect of the proposed algorithm is demonstrated in numerical simulations. The results show that it effectively provides good feasible solutions and finds an optimal one.

A review on fault-tolerant cooperative control of multiple unmanned aerial vehicles

This paper presents the recent developments in Fault-Tolerant Cooperative Control(FTCC)of multiple unmanned aerial vehicles(multi-UAVs).To facilitate the analyses of FTCC methods for multi-UAVs.the formation control strategies under fault-free flight conditions of multi-UAVs are first summarized and analyzed,including the leader-following,behavior-based,virtual structure,collision avoidance,algebraic graph-based,and close formation control methods,which are viewed as the cooperative control methods for multi-UAVs at the pre-fault stage.Then,by considering the various faults encountered by the multi-UAVs,the state-of-the-art developments on individual,leader-following,and distributed FTCC schemes for multi-UAVs are reviewed in detail.Finally,conclusions and challenging issues towards future developments are presented.

A review of cooperative path planning of an unmanned aerial vehicle group

As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks,e.g.,search and rescue,fire-fighting,reconnaissance,and surveillance.Cooperative path planning(CPP)is a key problem for a UAV group in executing tasks collectively.In this paper,an attempt is made to perform a comprehensive review of the research on CPP for UAV groups.First,a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements,i.e.,task,UAV group,and environment,as a basis for a comprehensive classification of different types of CPP problems.By following the proposed framework,a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified way.Then,a review and a statistical analysis are presented based on the taxonomy,emphasizing the coordinative elements in the existing CPP research.In addition,a collection of challenging CPP problems are provided to highlight future research directions.

Markov decision evolutionary game theoretic learning for cooperative sensing of unmanned aerial vehicles

As one of the major contributions of biology to competitive decision making,evolutionary game theory provides a useful tool for studying the evolution of cooperation.To achieve the optimal solution for unmanned aerial vehicles(UAVs) that are carrying out a sensing task,this paper presents a Markov decision evolutionary game(MDEG) based learning algorithm.Each individual in the algorithm follows a Markov decision strategy to maximize its payoff against the well known Tit-for-Tat strategy.Simulation results demonstrate that the MDEG theory based approach effectively improves the collective payoff of the team.The proposed algorithm can not only obtain the best action sequence but also a sub-optimal Markov policy that is independent of the game duration.Furthermore,the paper also studies the emergence of cooperation in the evolution of self-regarded UAVs.The results show that it is the adaptive ability of the MDEG based approach as well as the perfect balance between revenge and forgiveness of the Tit-for-Tat strategy that the emergence of cooperation should be attributed to.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

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.

Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

A cooperative detection game:UAV swarm vs.one fast intruder

This paper studies a special defense game using unmanned aerial vehicle(UAV)swarm against a fast intruder.The fast intruder applies an offensive strategy based on the artificial potential field method and Apollonius circle to scout a certain destination.As defenders,the UAVs are arranged into three layers:the forward layer,the midfield layer and the back layer.The co-defense mechanism,including the role derivation method of UAV swarm and a guidance law based on the co-defense front point,is introduced for UAV swarm to co-detect the intruder.Besides,five formations are designed for comparative analysis when ten UAVs are applied.Through Monte Carlo experiments and ablation experiment,the effectiveness of the proposed co-defense method has been verified.

Low Altitude Satellite Constellation for Futuristic Aerial-Ground Communications

This paper discusses the significance and prospects of low altitude small satellite aerial vehicles to ensure smooth aerial-ground communications for next-generation broadband networks.To achieve the generic goals of fifthgeneration and beyondwireless networks,the existing aerial network architecture needs to be revisited.The detailed architecture of low altitude aerial networks and the challenges in resource management have been illustrated in this paper.Moreover,we have studied the coordination between promising communication technologies and low altitude aerial networks to provide robust network coverage.We talk about the techniques that can ensure userfriendly control and monitoring of the low altitude aerial networks to bring forth wireless broadband connectivity to a new dimension.In the end,we highlight the future research directions of aerial-ground communications in terms of access technologies,machine learning,compressed sensing,and quantum communications.

基于改进鲸鱼算法的多无人机协同欺骗干扰技术

多机协同对组网雷达系统进行航迹欺骗干扰属于大规模优化问题,往往需要利用群体智能算法优化无人机的飞行任务,然而采用传统群体智能算法优化时往往会出现收敛速度慢、求解精度低等问题。针对这一问题,对标准鲸鱼优化算法进行了改进,提出了一种基于改进鲸鱼优化算法的多无人机协同欺骗干扰技术。首先建立了多无人机协同欺骗干扰组网雷达的数学模型以及对应的优化函数,然后在标准鲸鱼优化算法的基础上引入了自适应惯性权值,提高了算法的全局搜索能力和收敛速度。仿真实验表明,固定无人机数量为9架时,利用改进鲸鱼、标准鲸鱼、粒子群、蚁群4种算法分别优化多机协同欺骗干扰模型,得出改进鲸鱼优化算法平均运行时间最短,迭代次数最少,同时优化产生的实际航迹与理论值误差最小;逐步增加无人机数量至20架,利用上述四种算法进行模型求解时得出改进鲸鱼优化算法在不同无人机架数的条件下产生的假目标航迹条数均优于其他3种算法。

Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

无人机辅助无线通信网络资源自适应协同分配

为保证边缘用户可正常使用通信网络资源,研究了无人机辅助的无线通信网络资源自适应协同分配技术。构建无人机辅助无线通信网络模型,并设置自适应协同分配的目标函数,从而进行网络资源最大化的资源分配。引入排斥、吸引方法改进果蝇算法,提取最优、最差味道浓度与坐标信息。输出资源的最优传输速率、最优无人机运行轨迹的分配解,改进求解资源分配目标函数,完成通信网络资源自适应协同分配。试验结果表明,不同工况中,无人机能够结合用户分布状态飞行。在用户最小速率需求标准依次是3 bit/s、6 bit/s、9 bit/s时,经过6次迭代,算法便可收敛到纳什均衡点,无线通信任务时延便可控制为最小值1.5 s。该技术能够自适应协同分配无人机辅助无线通信网络资源。协同分配结果满足用户需求。

状态约束下的多飞行器有限时间姿态一致性控制

执行机构的物理限制等因素的影响,多飞行器姿态一致性控制面临姿态和角速度约束的挑战,加大了设计有效姿态一致性控制器的技术难度。针对此问题,提出了一种状态约束(含姿态和角速度约束)下的多飞行器有限时间姿态一致性控制器。利用非光滑理论和代数图论的知识设计了一类有限时间姿态一致性控制策略,并结合障碍Lyapunov函数技术构建了一类新颖的姿态一致性跟踪控制器。理论分析表明,提出的控制器能够在满足姿态和角速度约束条件下实现所有飞行器的姿态在有限时间内跟踪到期望姿态值。该方法具备结构简单、自定义约束适应性强和快速收敛等优点。仿真结果表明,所提的方法能够在满足姿态和角速度约束的条件下实现多飞行器对期望姿态的有限时间一致性跟踪。

有人/无人机一体化作战任务模型设计与实现

当前有人机与各类无人机灵活编组,为适应不同作战场景和作战需求,需设计不同作战任务模型。论文首先对有人/无人机一体化作战系统从功能划分、编组攻击方式等方面加以阐释。其次,提出任务模型构建原则,并在此基础上搭建基于作战流程的任务模型框架。最后,从行为逻辑、控制逻辑及行为规则解析等方面对任务模型的实现进行了探索。研究成果可为有人/无人机应对多样化战争需求提供模型参考,并可为战场上指挥者提供辅助决策。

有人/无人机协同电磁频谱作战问题研究

随着信息技术、无人机技术以及人工智能技术的不断发展,有人/无人机(UAV)协同电磁频谱作战将成为未来有人/无人协同作战体系中一种重要的运用方式。从有人/无人机协同电磁频谱作战的产生背景出发,剖析了有人/无人机协同电磁频谱作战的概念内涵及作战优势,探析了有人/无人机协同电磁频谱作战的运用模式,分析了其关键技术,可为我国有人/无人机协同电磁频谱作战的研究发展提供一定的借鉴和参考。

基于DQN-DDPG的空地协作边缘计算任务卸载与资源分配研究

在基础设施有限的地区或紧急救援场景中,无人机辅助的移动边缘计算被认为是一种有效的解决方案,可处理资源受限的智能设备的计算密集型任务和时延敏感性计算任务。考虑到地面基站和多无人机辅助的多用户空地协作移动边缘计算场景,提出一种联合优化用户关联、子信道分配及边缘服务器计算资源的分配方法,以最小化长期平均时延的任务卸载和资源分配方案。首先,根据用户的随机任务生成无人机移动方案,基于不同的卸载决策建立卸载计算模型和本地计算模型。然后,以最小化长期平均时延为优化目标优化问题。最后,结合DQN与DDPG提出一种基于混合深度强化学习DQN-DDPG的任务卸载和资源分配算法(HDCR),解决离散和连续变量之间的问题和混合决策问题。仿真表明,所提算法相较于基于离散决策的DDCR等算法,在减少平均时延方面性能更优。

基于改进粒子群优化算法的多无人机协同欺骗干扰技术

多无人机协同对组网雷达系统进行航迹欺骗干扰属于大规模优化问题,通常需要利用群体智能算法优化无人机的飞行任务。针对采用标准粒子群算法优化时出现的求解效率低下的问题,提出了一种基于改进粒子群优化算法的多无人机协同欺骗干扰技术。首先建立了多无人机协同欺骗干扰组网雷达的数学模型以及对应的优化函数。其次阐述了利用改进粒子群优化算法求解多机协同干扰模型的步骤。最后通过仿真实验对比了改进粒子群优化算法和标准粒子群优化算法在优化求解多机协同干扰模型的差异性。