Actual TDoA-based augmentation system for enhancing cybersecurity in ADS-B

Currently, cybersecurity and cyber resilience are emerging and urgent issues in next-generation air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast (ADS-B) owing to its low cost and high accuracy. Unfortunately, ADS-B is prone to cyber-attacks. To verify the ADS-B positioning data of aircraft, multilateration (MLAT) techniques that use Time Differences of Arrivals (TDoAs) have been proposed. MLAT exhibits low accuracy in determining aircraft positions. Recently, a novel technique using a theoretically calculated TDoA fingerprint map has been proposed. This technique is less dependent on the geometry of sensor deployment and achieves better accuracy than MLAT. However, the accuracy of the existing technique is not sufficiently precise for determining aircraft positions and requires a long computation time. In contrast, this paper presents a reliable surveillance framework using an Actual TDoA-Based Augmentation System (ATBAS). It uses historically recorded real-data from the OpenSky network to train our TDoA fingerprint grid network. Our results show that the accuracy of the proposed ATBAS framework in determining the aircraft positions is significantly better than those of the MLAT and expected TDoA techniques by 56.93% and 48.86%, respectively. Additionally, the proposed framework reduced the computation time by 77% compared with the expected TDoA technique.     

未来智能空战发展综述

随着装备战斗力生成模式逐渐向机械化、信息化、智能化"三化融合"发展演变,未来航空主战装备的定位、形态及运用将可能发生根本性变革。为应对新时期空战任务所面临的环境高复杂性、博弈强对抗性、响应高实时性、信息不完整性、边界不确定性等一系列挑战,交叉融合人工智能理论与空战对抗技术,研发智能空战系统,将有望在下一代无人制空装备谱系中构建不对称"智能代差",成为制胜未来空天战场的核心关键。本文完整梳理了智能空战研究的发展脉络,总结了以专家机动逻辑、自动规则生成、规则演进、机器学习等方法为代表的智能空战基础理论。从体系、应用及技术视角全面剖析了智能空战的发展趋势,以智能空战的不确定性、安全性、解释性、迁移性、协同性为切入点阐述了智能空战应用落地的若干问题,以期为未来智能空战技术研究勾勒出一条新的探索路径,为人工智能理论与航空科学技术的跨领域交叉融合提供新的发展思路。     

高职院校学习型辅导员队伍构建策略探析

在高职院校,普遍存在着辅导员学习研究意识淡漠,缺乏学科背景支持、学习氛围不浓等问题,针对这些问题,高职院校须构建一支学习型辅导员队伍,可采取的具体策略包括：建设学习型学生工作领导队伍、培养问题研究意识、加强团队学习和培训指导等策略。     

The perfect boring situation—Addressing the experience of monotony during crewed deep space missions through habitability design

In contemporary orbital missions, workloads are so high and varied that crew may rarely experience stretches of monotony. However, in historical long duration missions, occurrences of monotony were, indeed, reported anecdotally by crew. Of the effective countermeasures that appear to be at hand, many rely on visual or logistical proximity to the Earth, and are not feasible in the remote context of an extended deep space mission scenario. There, particularly in- and outbound cruising stages would be characterised by longer, comparably uneventful periods of low workload, coupled with confinement and unchanging vehicle surroundings.     

A machine learning approach to crater detection from topographic data

Craters are distinctive features on the surfaces of most terrestrial planets. Craters reveal the relative ages of surface units and provide information on surface geology. Extracting craters is one of the fundamental tasks in planetary research. Although many automated crater detection algorithms have been developed to exact craters from image or topographic data, most of them are applicable only in particular regions, and only a few can be widely used, especially in complex surface settings. In this study, we present a machine learning approach to crater detection from topographic data. This approach includes two steps: detecting square regions which contain one crater with the use of a boosting algorithm and delineating the rims of the crater in each square region by local terrain analysis and circular Hough transform. A new variant of Haar-like features (scaled Haar-like features) is proposed and combined with traditional Haar-like features and local binary pattern features to enhance the performance of the classifier. Experimental results with the use of Mars topographic data demonstrate that the developed approach can significantly decrease the false positive detection rate while maintaining a relatively high true positive detection rate even in challenging sites.     

系绳辅助的行星际轨道捕获研究

针对深空探测中应用动量交换系绳辅助进行行星际轨道捕获时的系绳控制问题,首先对运行在目标行星双曲线飞越轨道上的探测器系统进行动力学建模,给出了一致性轨道捕获条件和系绳最佳切断点,并进行了动力学特性分析。考虑到子探测器捕获后的变轨需求及系绳收放速率的限制,提出了新的最优控制方法,并应用模拟退火算法进行了数值求解。仿真结果表明,系绳切断时指向恰当,子探测器距离目标行星最近,将有利于后续变轨;系绳最大收放速率约为30m/s,切实可行。     

深空通信中的网络技术

介绍了深空通信的特点、目前空间链路所使用的数据传输协议对深空探测环境的适用性、新制订容延迟网络体系结构的设计思想和实现要点,以及有待进一步研究的内容。     

基于网络语言学习中心的公共英语口语辅助教学设计

为了推进高校公共英语教学,以国家示范性院校建设为契机,学院建立了英语语言学习中心,开始了结合网络自主学习概念的公共英语教学改革的尝试。英语口语教学作为公共英语教学的主要组成部分,主要是以基于网络语言自主学习的辅助教学展开设计方案。     

深空探测器约束简化与任务规划方法研究

针对深空探测器现有的任务规划方法在处理系统间复杂约束时存在的约束复杂度高、实时响应能力差、计算效率低等问题,提出一种新的约束简化方法和启发式连续任务规划方法。首先,在时间线规划模型中根据两两子系统间的实时状态关系定义启发式因子,并利用该因子在规划周期内的取值建立子系统间时间线临时从属关系,从而合理地降低规划过程中的约束复杂程度;然后,在规划算法中采用时间线状态扩展策略,根据时间线临时从属关系对各子系统间的状态进行横向和纵向扩展,从而实现对目标任务规划进行快速排序。仿真结果表明由启发式因子建立的时间线临时从属关系有效简化了任务规划过程中的时间约束和资源约束、提高了任务规划的效率和灵活性。     

基于过程神经网络的液体火箭发动机状态预测

提出一种基于极限学习算法的离散过程神经网络模型,用于解决液体火箭发动机状态预测这一难题。首先,在历史数据的基础上建立离散过程神经网络（DPNN）预测模型;然后,根据在线更新的数据样本,采用递推极限学习（EL）算法对双并联前馈离散过程神经网络（DPFDPNN）隐层到输出层的权值进行更新,并应用权值更新后的过程神经网络对发动机状态进行预测;最后,以液体火箭发动机状态预测中氢涡轮泵扬程预测为例,分别采用有权值更新和无权值更新两种预测模型进行了试验。结果表明,通过更新过程神经网络权值可以使模型具有更高的预测精度和更好的适应能力,该方法能够为液体火箭发动机状态预测提供一种有效的解决途径。