基于UAV的空地协同任务卸载策略研究

随着各行业对大批量信息处理需求的增加,移动边缘计算（Mobile Edge Computing, MEC）技术应运而生。而在陆地障碍较多、MEC服务器搭载不便的情况下,研究了一种无人机（Unmanned Aerial Vehicle, UAV）中继辅助用户卸载任务到基站的场景。针对该场景,提出了一种基于博弈论的最优任务卸载方法,通过联合优化任务卸载比例和卸载策略使得系统时延最小化。由于这两个变量之间相互耦合,因此将原优化问题转化为两个子问题求解。首先,在确定策略的情况下,证明了系统时延最小值存在的条件,得到了用户的最优卸载比例闭合解。然后,将原优化问题转化为任务分配问题,并建立博弈论模型。在证明了该模型存在纳什均衡（Nash Equilibrium,NE）的前提下,经过多次迭代,求解得到基于时延最小的用户任务卸载策略集。仿真结果表明：上述方法有效降低了全局计算时延,在时效性上优于其他一些常见的卸载方法。     

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。     

无人机的新颖机翼-前掠翼

飞机采用前掠翼气动布局并非鲜为人知，但无人驾驶飞机采用前掠翼却前所未有。本文通过对前掠翼的气动、结构等特性分析及其关键技术的解决，阐述了前掠翼的应用价值和广阔前景．为未来无人机设计开辟了一条新的领域。     

一种隐身无人飞行器外形的电磁散射特性的实验研究

以降低无人飞行器侧向雷达截面（RCS）为目的，根据电磁散射基本理论和低RCS选型的基本原理，设计了一种以椭圆截面为机身、大后掠角的翼身和边条融合的鸭式布局外形。电磁散射性的实验结果表明，该外形的RCS在侧向比基准模型的降低了10dB左右。     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

Control techniques of tilt rotor unmanned aerial vehicle systems:A review

The tilt rotor unmanned aerial vehicle (TRUAV) exhibits special application value due to its unique rotor structure. However, varying dynamics and aerodynamic interference caused by tiltable rotors are great technical challenges and key issues for TRUAV's high-powered flight con-trols, which have attracted the attention of many researchers. This paper outlines the concept of TRUAV and some typical TRUAV platforms while focusing on control techniques. TRUAV struc-tural features, dynamics modeling, and flight control methods are discussed, and major challenges and corresponding developmental tendencies associated with TRUAV flight control are summa-rized.     

Highly reliable relative navigation for multi-UAV formation flight in urban environments

Formation flight of multiple Unmanned Aerial Vehicles (UAVs) is expected to bring significant benefits to a wide range of applications. Accurate and reliable relative position information is a prerequisite to safely maintain a fairly close distance between UAVs and to achieve inner-system collision avoidance. However, Global Navigation Satellite System (GNSS) measurements are vulnerable to erroneous signals in urban canyons, which could potentially lead to catastrophic consequences. Accordingly, on the basis of performing relative positioning with double differenced pseudoranges, this paper develops an integrity monitoring framework to improve navigation integrity (a measure of reliability) in urban environments. On the one hand, this framework includes a fault detection and exclusion scheme to protect against measurement faults. To accommodate urban scenarios, spatial dependence in the faults are taken into consideration by this scheme. On the other hand, relative protection level is rigorously derived to describe the probabilistic error bound of the navigation output. This indicator can be used to evaluate collision risk and to warn collision danger in real time. The proposed algorithms are validated by both simulations and flight experiments. Simulation results quantitatively reveal the sensitivity of navigation performance to receiver configurations and environmental conditions. And experimental results suggest high efficiency and effectiveness of the new integrity monitoring framework.     

Optimal trajectory and downlink power control for multi-type UAV aerial base stations

Unmanned Aerial Vehicles (UAVs) enabled Aerial Base Stations (UABSs) have been studied widely in future communications. However, there are a series of challenges such as interference management, trajectory design and resource allocation in the scenarios of multi-UAV networks. Besides, different performances among UABSs increase complexity and bring many challenges. In this paper, the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated. In order to satisfy the signal to interference plus noise power ratio of users, each UABS needs to adjust its position and transmission power. Based on the interactions among multiple communication links, a non-cooperative Mean-Field-Type Game (MFTG) is proposed to model the joint optimization problem. Then, a Nash equilibrium solution is solved by two steps: first, the users in the given area are clustered to get the initial deployment of the UABSs; second, the Mean-Field Q (MFQ)-learning algorithm is proposed to solve the discrete MFTG problem. Finally, the effectiveness of the approach is verified through the simulations, which simplifies the solution process and effectively reduces the energy consumption of each UABS.     

基于混合量子粒子群优化算法的三维航迹规划

针对粒子群优化算法(PSO)存在的早熟收敛问题,通过将种群的繁殖机制引入量子粒子群优化算法(QPSO),提出了一种混合量子粒子群优化算法(HQPSO),将该算法应用于无人飞行器的三维航迹规划。同时,运用统计学方法,通过仿真实验比较了HQPSO算法与QPSO算法以及带动态变化惯性权系数的PSO算法的性能。仿真实验结果表明,HQPSO不但比QPSO和PSO具有更强的全局搜索能力,而且比QPSO和PSO具有更快的收敛速度。     

空间平台与卫星成本的比较

七十年代末推出的空间平台是无人航天器的新品种。它与人造卫星的差别在于:一是综合性,能同时或轮流搭载不同性质的有效载荷;二是能用航天飞机回收后重复使用,或通过接受在轨道服务而长期(十年以上)使用。与一次性使用的卫星相比,重复使用和长期使用的空间平台是否经济,不可一概而论,需要具体分析。本文给出了一次使用卫星、重复使用空间平台和长期使用空间平台的各类成本和全寿命成本的估算数学模型(CER—cost estimation relationship)。最后通过实例计算比较这三种航天器的全寿命成本,并指出它们各自在什么条件下最经济。