Optimization of bits allocation and path planning with trajectory constraint in UAV-enabled mobile edge computing system

In this paper, an Unmanned Aerial Vehicle (UAV) enabled Mobile Edge Computing (MEC) system is studied, in which UAV acts as server to offer computing offloading service to the Mobile Users (MUs) with limited computing capability and energy budget. We aim to minimize the total energy consumption of MUs by jointly optimizing the bit allocation for uplink, computing at the UAV and downlink, along with the UAV trajectory in a unified framework. To this end, a trajectory constraint model is employed to avoid sudden changes of velocity and acceleration during flying. Due to high-order information in use, we lead to a more reasonable nonconvex optimization problem than prior arts. An Alternating Direction Method of Multipliers (ADMM) method is introduced to solve the optimization problem, which is decomposed into a set of easy sub-problems, to meet the requirement on the efficiency in edge computing. Numerical results demonstrate that our approach leads a smoother UAV trajectory, significantly save the energy consumption for UAV during flying.     

太阳能飞机循环飞行的高度剖面能量策略

为满足设定的太阳能飞机多日连续飞行条件，依据飞行过程中当前时刻的飞行高度、光伏输出功率、动力电池组余量等系统状态参数，研究如何分配动力电池组充放电和电推进系统输入等功率。所用策略立足于实时功率平衡，充分利用正午前后的光伏峰值功率用于飞机爬升及充电，在午后下滑过程中利用全部光伏输出，以最大化利用光伏资源；在光伏有效输出不足时则以一定的维持功率下滑，使能量的综合损失最小。方法能够提高以预定夜间飞行高度连续多日续航的成功率，提升飞行高度、纬度、季节范围或搭载能力，或者拓展这几种飞行条件的组合域，优化太阳能飞机的适用性。     

无人机自由编队飞行的补偿模糊神经网络控制

为了克服当前提出的无人机编队飞行的不足，提出一种新的无人机编队飞行概念——自由编队飞行，制定了相关的避让规则，用补偿模糊神经网络进行训练，实现自由编队飞行的补偿模糊神经网络控制。仿真显示，编队飞行的飞机能按照规则自主改变航路进行避让，表明了该方法的有效性。     

一种低RCS无人飞行器外形的气动特性实验研究

根据气动布局的基本原理,结合低RCS要求,设计了一种鸭式布局、带边条的翼身融合无人飞行器外形,实验结果表明,该外形不仅具有低RCS特征,而且具有良好的气动特性,升阻比达到8左右。     

多无人机监控航路规划

为了提高监控任务的效率，在执行任务前必须规划设计出高效的无人机飞行航路。针对这一问题，考虑在任务区域内进行多无人机监控航路优化存在计算的复杂性和收敛性以及计算数据量限制等问题，采用分散式方法对监控航路进行了优化，并以无人机的监控任务为例提出了一种监控效率指标评估的计算方法，解决了航路规划中的监控效率量化问题。通过该方法得到的无人机监控任务的飞行航路可以有效地提高无人机的监控效率。     

应用极值搜索算法优化无人机近距离编队飞行

针对无人机近距离编队飞行问题，采用极值搜索算法．解决其中僚机所需动力最小化的控制问题。在近距离飞行编队中，充当僚机的无人机会受到前面长机产生的旋涡力的影响，旋涡力是两飞机间距离的函数，以僚机的俯仰角作为搜索目标，寻求两无人机问的最大旋涡力．使得僚机所消耗动力最小，从而构造出最优的飞行编队结构。通过仿真．验证了所设计的控制方法能够极大地节省僚机的能量。     

无人机近距离编队飞行模型建立及控制器设计

针对无人机编队飞行问题，提出了无人机近距离编队飞行的建模方法。采用全状态量反馈．同时将气动干扰作为非线性反馈，设计出一套适用于无人机近距离编队飞行的自动驾驶仪。通过仿真验证，表明此控制器能较好地稳定优化后的编队飞行结构，并具有良好的鲁棒性。     

高空高速无人飞行器热控制系统设计

针对飞行时间短、速度和高度变化快、表面温度波动大的无人飞行器UAV(Unmanned Aerial Vehicles)热控制系统设计难题,提出了一种可解决实际工程问题的热分析计算方法.即把热天工况、冷天工况和标准天工况作为设计/试验工况;采用参考温度法、高超音速工程预测法或计算流体动力学CFD(Computational Fluid Dynamics)数值模拟法,确定了飞行器表面温度分布,并把其作为后续热分析数学模型的外边界条件;分析结构热容量对瞬态热载荷的影响,建立与之相应的边值问题方程,并采用有限差分法求解;根据高空高速飞行特点及瞬态热载荷值,确定仪器设备舱调温系统方案.     

无人机高速DS/FH混合扩频通信系统关键模块设计

针对UAV(Unmanned Aerial Vehicle,无人机)测控链路高抗干扰需求,给出了实现跳频速率20 000次/s的扩跳混合无人机测控数据链系统实现方案.系统采用跳频频率并行捕获模式.跳频源设计采用直接频率合成、DDS(Direct Digital Synthesis,直接数字频率合成)和PLL (Phase-Locked Loop,锁相环)相结合的混合频率合成技术.利用相位响应滤波器,通过正交调制技术完成MSK(Minimum Shift Keying,最小频移键控)、GMSK (GaussianMinimum Shift Keying,高斯最小频移键控)调制.通过推导得出2 bit差分解调输出与载波频率选择有关,且为四分之数据率的整数倍关系.然后通过实例分析给出了跳频频率间隔选取方法,即跳频间隔应当将临近跳频的主瓣分开,同时跳频载波频率应当设在其他跳频频谱的零点处.最后总结了高速扩跳频系统参数设计一般步骤.     

Flight safety measurements of UAVs in congested airspace

Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles (UAVs) in a complex environment. A probabilistic approach is proposed to measure safety situation in congested airspace. The occupancy distribution of the airspace is represented with conflict probability between spatial positions and UAV. The concept of a safety envelope related to flight performance and response time is presented first instead of the conventional fixed-size protected zones around aircraft. Consequently, the conflict probability is performance-dependent, and effects of various UAVs on safety can be distinguished. The uncer-tainty of a UAV future position is explicitly accounted for as Brownian motion. An analytic approximate algorithm for the conflict probability is developed to decrease the computational consumption. The relationship between safety and flight performance are discussed for different response times and prediction intervals. To illustrate the applications of the approach, an experi-ment of three UAVs in formation flight is performed. In addition, an example of trajectory planning is simulated for one UAV flying over airspace where five UAVs exist. The validation of the approach shows its potential in guaranteeing flight safety in highly dynamic environment.