基于航空照相枪的数字暗盒设计

航空照相枪是一种安装在歼击机上的小型电影摄影机。传统照相枪主要存在判读环境受限制、判读准确度低、工作可靠性差、效率低等问题。设计了由百万像素CMOS图像传感器、大容量闪存FLASH和CPLD为主要部件组成的数字暗盒。使用基于CPLD状态机设计技术,4片闪存分时存储数据,实现无缓存无压缩实时存储,可以有效地提高判读效率、准确度问题,并使计算机自动判读成为可能。     

基于三分图的非共面卫星分布式加注任务规划

分布式加注规划的目的在于规划加注过程的交会路径,使任务在满足约束的情况下整体燃料消耗最优。针对异面圆轨道卫星间的分布式加注策略,建立了分布式加注任务规划问题的数学模型,把该规划问题归结为非完全赋权三分图的匹配问题,并将整体最少燃料消耗作为规划目标。其次,进行了算法的流程设计,采用了Kuhn-Mundres图论算法和整数遗传算法相结合的LSGA算法加快了收敛的速度保证了全局最优。最后,选取了两个具有小角度轨道偏差的异面卫星星座对该问题进行分析。计算得到了优化后的双冲量机动下加注任务的服务关系与燃料代价,规划算法的有效性也得到了验证。     

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.     

低温推进剂在轨加注技术与方案研究综述

为了探究适用于低温推进剂在轨加注的相关技术与方案,通过文献调研与对比分析,介绍国内外在轨加注技术的研究现状,梳理低温推进剂在轨加注的关键技术,研究现有加注技术与方案对低温推进剂的适用性,并提出我国开展相关研究的思路与方向。研究表明：1）气液分离、蒸发量控制、质量测量和流体驱动循环等技术是直接影响推进剂在轨加注系统结构与加注性能的关键技术;2）低温推进剂具有沸点低、表面张力小等特殊性,对气液分离、系统热防护等技术的性能要求更高;3）表面张力式气液分离、纤维镜或射频质量检测、多层隔热材料、热力学排气系统（TVS）以及无排气加注等先进技术方案对低温流体和微重力环境均具有更好的适用性,将成为实现低温推进剂在轨加注的关键突破口。     

加油机喷流对受油机的气动干扰机理

针对软式平台(HDB)、软式吊舱(HDP)和硬式平台(FB)3种典型加油方案研究了喷流对加油流场的影响。采用结构化多块网格和雷诺平均Navier-Stokes(RANS)方程,通过带涡轮动力模拟器(TPS)和翼身组合体构型DLR-F6对数值方法进行了验证。通过对3种典型的空中加油方案进行数值模拟,并和不考虑喷流效应的空中加油流场进行对比,详细研究了不同加油方案中加油机喷流对受油机气动特性的干扰作用。结果表明:受油机升力系数、阻力系数以及低头力矩与无喷流相比均有增大。受油机在硬式平台加油方案中受喷流影响最小,但其横航向气动特性在软式吊舱加油方案中受加油机喷流影响较大。喷流对受油机的气动干扰主要表现在提高受油机来流动压、改变局部迎角以及对周边气流的引射作用,这些因素会导致受油机表面压力与无喷流相比发生较大变化。其中,局部迎角的改变是由喷流的加速效应及其对尾涡的耗散作用导致的。     

基于自适应动态逆的自动空中加油轨迹跟踪

针对自动空中加油(AAR, Automated Aerial Refueling)的受油机轨迹跟踪控制问题,采用导引环与姿态控制环分离的控制策略,提出了基于非线性路径跟随导引和自适应神经网络动态逆的受油机轨迹跟踪控制方法.该方法将改进的非线性导引应用于受油机横、纵向导引控制,使用自适应神经网络在姿态角速率回路补偿外界干扰和系统模型误差,并利用比例-积分型姿态角速率误差动态方程设计自适应神经网络权值更新律.仿真结果表明:在受油机接近加油机过程中,该方法有效提高了受油机轨迹跟踪控制系统的抗干扰能力和对模型不确定性的自适应能力,能够满足自动空中加油的控制要求.     

无人机自动空中加油精确对接控制

针对无人机自动空中加油对接段的精确控制问题,提出了无人机自动空中加油的控制方案和策略,并在分析加油机尾涡干扰和加油锥套自由摆动及其对受油机影响的基础上,设计了适用于自动空中加油精确对接控制的受油机参考轨迹发生器和轨迹跟踪控制器.轨迹跟踪控制器采用线性二次型调节器方法,并充分考虑了加油过程中各种干扰的影响.仿真结果表明,该导引控制方法具有良好的快速性和抗干扰能力,可以满足空中加油的任务要求.      

俄罗斯空间站推进剂补加程序分析

补加程序是推进剂补加系统的关键技术之一,而目前也仅有俄罗斯有成功应用的经验.根据目前获取的资料,经过计算、仿真和论证,对俄罗斯空间站的补加系统进行了研究,分析了ATV对空间站进行推进剂补加的程序,初步得到了俄罗斯空间站推进剂补加的特点,可作为目前我国空间站方案论证期间补加程序的参考.     

N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently, deep learning has been widely utilized for object tracking tasks. However, deep learning encounters limits in tasks such as Autonomous Aerial Refueling (AAR), where the target object can vary substantially in size, requiring high-precision real-time performance in embedded systems. This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem. First, an Asymmetric Convolutional Network (ACNet) and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background. The prior object information, such as its location in the previous frame and its speed, is utilized to adaptively track objects of various sizes. Moreover, based on the n-fold Bernoulli probability theorem, we develop a filter that uses statistical laws to reduce the false positive rate of object tracking. To evaluate the efficiency of our algorithm, a new AAR dataset is collected, and extensive AAR detection and tracking experiments are performed. The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks; the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.