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.     

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.     

电动帆航天器谷神星探测任务轨迹优化

针对电动帆航天器谷神星探测任务轨迹优化问题,提出一种基于高斯伪谱法和遗传算法的混合优化算法。为了验证所提出的混合优化算法有效性,并考察任务起始时间和电动帆特征加速度对探测任务的影响,进行了一定数量的数值仿真。仿真结果表明：电动帆航天器自地球至谷神星的飞行时间随着起始时间的变化呈周期性波动,波动周期基本与地球和谷神星的会合周期一致;电动帆航天器的特征加速度越小,完成过渡所需要的飞行时间越长,且一个具有中等特征加速度的电动帆航天器便能在可接受的时间内完成自地球至谷神星的过渡;所提出的混合优化算法是有效的,能够在无任何初值猜测的情况下完成电动帆航天器飞行轨迹的优化。     

基于WPF的遥测CAS信息实时监控系统设计开发

在当前试飞模式下,立足于某型客机机组告警系统(Crew Alerting System,简称CAS)信息复杂、重构平台单一等不足,设计了一套CAS信息实时监控系统。系统基于WPF(Windows Presentation Foundation,简称WPF)框架,采用数据访问层、业务逻辑层和显示层组合的典型三层架构,以遥测脉冲编码调试(Pulse Code Modulation,以下简称PCM)数据为输入,结合DataBinding数据驱动模型和XAML等技术实现了CAS信息的实时解析、传输、分发及显示。同时,极大地降低了系统间的耦合性,提高了实时性能和系统的可维护性。通过滑行、首飞等试验的充分验证,系统实现了该型客机CAS信息的100%地面复现,为后续民机试飞测试、监控技术的创新提供了参考依据。     

一种基于多无人机的中继节点布置问题建模与优化方法

针对战场环境下急需在无法通信的节点间构建有效通信链路的情形,使用多无人机作为中继节点,建立了中继节点布置(RNP)问题模型。模型以中继链路有效和无人机安全为约束,以中继布置点位置及相应的无人机为输出,不但考虑了使用的中继无人机数量,还考虑了构建中继链路花费的时间。考虑到该问题是难以求解的混合整数多目标优化问题,同时在紧急应用情形下,要求求解算法快速有效,建立了一种多项式时间中继节点布置算法(PTRPA)。仿真实验验证了所提模型确实能够在更短的时间内完成有效中继链路构建;通过Monte-Carlo方法对比和分析不同因素对PTRPA算法、随机抽样算法、遗传算法求解该问题的结果性能和时间性能的影响,验证了PTRPA算法不但能够给出接近最优的解,且快速有效,满足战场决策需求。     

基于GA-OCPA学习系统的无人机路径规划方法

为解决未知空域中无人机路径规划方法实时性和适用性不足的问题,以生物应激条件反射理论为基础,将无人机实时路径规划类比为在外界条件刺激下的一种自学习行为。首先,将概率自动机与遗传算法相结合,设计了基于Skinner操作条件反射理论框架(GA-OCPA)的学习系统;然后,将无人机规避机动的飞行速度、滚转加速度和拉升加速度作为系统学习的行为,并计算每次学习尝试之后的选择概率和个体适应度,通过遗传算法搜索最优行为进而得到最优路径;最后,运用增量多层判别回归树(IHDR)对学习得到的最优行为建立知识库,形成威胁状态与路径规划的匹配映射。实验结果表明GA-OCPA学习系统对于无人机路径规划具备有效性和适用性。     

多植保无人机协同路径规划

为实现多植保无人机（UAVs）协同作业,并提高作业效率,提出了一种基于改进粒子群优化（PSO）的多植保无人机协同路径规划算法。根据作业区域的形状面积和植保UAV的作业参数划分各架UAV作业区域,采用栅格法生成各区域全覆盖作业航线。以各架植保UAV各架次植保作业距离为算法寻优变量,在确保各架UAV补给时间满足间隔分布约束条件下,综合考虑补给总次数、返航补给总时间、总耗时和最小补给时间间隔4项因素,并构成目标函数,通过采用改进PSO算法,实现了对各UAV返航顺序和返航点位置的寻优。仿真分析结果表明,相较于最大作业距离规划和最小返航距离规划,本文提出的规划算法表现出了较优的性能和较好的作业区域适应性,证实了其有效性和实用性。     

深空测控通信的特点和主要技术问题

分析研究了深空测控通信的特点,对频段提高、灵敏度提高、G/T值提高、EIRP提高、编码增益提高、定轨精度提高、极窄带锁相、低门限接收和调制/解调方式等主要技术问题进行了探讨.     

轮履复合式军用地面探测车运动学建模及分析

为更好的对军用探测车运动过程进行控制,提出了轮履复合式探测车运动学模型的构建方法.通过对轮履复合式移动系统结构进行分析,首先建立了探测车三维运动学模型,并推出了其运动方程;其次建立了探测车车体速度与车轮速度之间的雅可比矩阵,利用最小二乘法对车体速度进行求解,提供了获得探测车越障过程中位置和方位的方法.最后对探测车行进过程进行了运动仿真,数值仿真分析验证了运动学模型的正确性及轮履复合式移动系统的运动特性.      

高超声速飞行器混合灵敏度控制器设计

由于高超声速飞行器飞行过程中存在无法预知的扰动和不确定因素,选用H∞鲁棒控制理论进行控制系统设计。研究了H∞混合灵敏度优化设计方法,选取适当的加权函数,根据＂2-Riccati方程＂利用Matlab鲁棒工具箱设计控制器。仿真结果表明,采用混合灵敏度设计方法设计出的控制器能够满足设计指标的要求,达到很好的控制效果。