行星软着陆GPS有模型强化学习制导方法

由于距离地球较远、测控延时误差较大、飞行环境十分复杂且难以提前预测，行星软着陆的自主制导技术目前存在水平位置估计困难、导航参考信息匮乏、复杂地形着陆困难等挑战。针对行星软着陆存在的困难和挑战，提出了基于引导策略搜索算法的有模型强化学习制导方法，实现了着陆器在初始状态受到扰动时，无需重新规划，仍能在满足约束条件的情况下降落在指定位置。该方法将迭代线性二次调节器作为控制器，产生初始轨迹；其次，使用多层神经网络拟合制导策略；最后，利用控制器监督策略学习，进而收敛产生可行策略。针对行星表面软着陆的仿真验证结果显示该算法仅通过几次循环，即可以实现初始状态变化的快速软着陆。一方面表明了基于有模型强化学习的数据高效利用率，另一方面也证明了强化学习方法在深空探测领域中具有广阔的应用前景。     

基于PCA-MPSO-ELM的空战目标威胁评估

目标威胁评估是空战对抗过程中的关键环节。由于影响空战目标威胁评估的因素复杂多样，且指标之间存在相关性，导致传统的评估算法无法得到准确客观的评估结果。由此，提出了一种基于主成分分析法和改进粒子群算法优化的极限学习机（PCA-MPSO-ELM）的目标威胁评估算法。首先，综合分析了影响目标威胁程度的指标，利用主成分分析法对原始评估指标进行线性变化处理得到综合变量，消除了评估指标之间的相关性，实现了对评估数据的降维；在此基础上，构建ELM神经网络并利用改进的粒子群算法优化极限学习机的输入权值和阈值，提高了目标威胁评估模型的精度。最后，在空战训练测量仪中选取空战对抗数据，利用威胁指数法构造了目标威胁评估样本数据，通过仿真实验分析了PCA-MPSO-ELM算法的精度和实时性，结果表明所提算法可以快速准确地进行空战目标威胁评估。     

自主空战技术中的机动决策:进展与展望

自主空战（AAC）是指飞机依靠机载等相关设备，自主进行战场感知、决策及控制，以执飞空战的技术，其核心是机动决策模块。针对该模块的研究，调研了国内外的现有方法，并按照有关方法的核心内涵，将其分为基于数学求解、机器搜索、以及数据驱动3类分别简要概述；针对每一类方法列出了具有代表性的示例技术，并讨论了其优缺点。指出了自主空战决策的研究应立足现有相关方法及技术基础，同时积极吸纳机器学习、人工智能等新兴技术，合理利用各类方法的优点，取长补短，实现自主空战决策的长足发展；有关于该研究设想的关键科学问题及其潜在解决方案也在文中进行了初步讨论。希冀基于针对自主空战决策研究框架及实现方法的概念性讨论，促进其技术发展，推广相关思路在有关国家安全、国民经济建设等领域的应用，以满足国家重大需求和工程实践。     

Heading south with a north-up map: A choice processing analysis of map alignment effects

This study employed an information accumulation model of choice reaction times to investigate alignment effects in mental representations of maps. University students studied a map from a single orientation (with north at the top). In a subsequent two-choice reaction time task, the students’ spatial knowledge of the map was assessed employing spatial left/right judgments, which were made from imagined perspectives that were either north-aligned or south-aligned. Data showed a standard alignment effect, favoring north- over south-aligned trials. To examine the locus of this effect, data were fit using the Linear Ballistic Accumulator (LBA) model of speeded decisions (Brown & Heathcote, 2008). Of interest were three model parameters: drift rate, the speed at which evidence accumulates toward a response; response threshold, the amount of evidence demanded from the decision maker before selecting a response; and non-decision time, the time consumed by pre- and postdecisional processes. The best-fitting model suggested that non-decision time accounted for the alignment effect. The difference in non-decision time between north and south-aligned judgments suggests a mental alignment stage on south-aligned trials, accounting for the longer reaction times for judgements misaligned with the presented north orientation of the map.     

小型核反应堆自主控制及其深空探测应用设想

针对空间小型模块化核反应堆的自主控制问题，提出自主控制体系结构，降低反应堆控制对人的依赖程度，满足了深空探测任务对空间堆自主控制的需求。首先介绍了核反应堆自主控制技术和空间探测自主技术的发展现状，分析了空间小型堆的自主控制需求，然后阐释了自主控制及核反应堆近自主控制的内涵。最后基于空间堆的运行特点，给出小型堆近自主控制分层体系结构的组成元素，并进一步建立了融合决策层和功能层的小型堆多智能体自主控制体系结构。     

Multi-block SSD based on small object detection for UAV railway scene surveillance

A method of multi-block Single Shot MultiBox Detector (SSD) based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance. To address the limitation of small object detection, a multi-block SSD mechanism, which consists of three steps, is designed. First, the original input images are segmented into several overlapped patches. Second, each patch is separately fed into an SSD to detect the objects. Third, the patches are merged together through two stages. In the first stage, the truncated object of the sub-layer detection result is spliced. In the second stage, a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers. The boxes that are not detected in the main-layer are retained. In addition, no sufficient labeled training samples of railway circumstance are available, thereby hindering the deployment of SSD. A two-stage training strategy leveraging to transfer learning is adopted to solve this issue. The deep learning model is preliminarily trained using labeled data of numerous auxiliaries, and then it is refined using only a few samples of railway scene. A railway spot in China, which is easily damaged by landslides, is investigated as a case study. Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6% and obtains an improvement of up to 9.2% compared with the traditional SSD.     

Shocklike soliton because of an impinge of protons and electrons solar particles with Venus ionosphere

This paper introduces an investigation of shocklike soliton or small amplitude Double Layers (DLs) in a collisionless plasma, consisting of positive and negative ions, nonthermal electrons, as well as solar wind streaming protons and electrons. Gardner equation is derived and its shocklike soliton solution is obtained. The model is employed to recognize a possible nonlinear wave at Venus ionosphere. The results indicate that the number densities and velocities of the streaming particles play crucial role to determine the polarity and characteristic features (amplitude and width) of the shocklike soliton waves. An electron streaming speed modifies a negative shocklike wave profile, while an ion streaming speed modulates a positive shocklike wave characteristic.     

跨传感器异步迁移学习的室内单目无人机避障

针对强化学习策略由仿真环境向实际迁移困难的问题,以提高无人机采用无深度信息单目视觉时的行人规避能力为目标,提出一种基于异步深度神经网络结构的跨传感器迁移学习方法。首先,在仿真环境中仅使用虚拟单线激光雷达作为传感器,通过基于确定性策略梯度(DDPG)的深度强化学习方法,训练得到一个稳定的初级避障策略。其次,用单目摄像头和激光雷达同步采集现实环境中的视觉和深度数据集并逐帧绑定,使用上述初级避障策略对现实数据集进行自动标注,进而训练得到无需激光雷达数据的单目视觉避障策略,实现从虚拟激光雷达到现实单目视觉的跨传感器迁移学习。最后,引入YOLO v3-tiny网络与Resnet18网络组成异步深度神经网络结构,有效提高了存在行人场景下的避障性能。     

一种高速可变形飞行器智能变形决策方法

针对一类高速可变形飞行器(HMFV)的变形决策问题,提出一种基于深度确定性策略算法(DDPG)下考虑综合性能指标最优的智能变形决策方法。首先,以一类后掠角可连续变化的高速飞行器为研究对象,给出变形飞行器动力学模型,分析模型特性及变形量与关键气动参数之间的定性关系。其次,基于关键气动数据特征分析,考虑包含气动性能、控制误差在内的综合性能指标,设计一种基于DDPG算法的智能变形决策方案。再者,针对带有标称控制器的HMFV进行变形决策训练,实时获得滑翔过程中不同飞行状态下的最优构型。最后,仿真结果表明所设计的智能变形决策算法收敛效果好,且具备较好的泛化性能。相比于固定外形,可通过变形使得在不同状态下的升阻比保持最优,且与考虑单一决策指标相比,考虑综合指标最优的变形决策可进一步缩小姿态动态跟踪误差。     

遥感小卫星简介——以"北京一号"小卫星为例

高性能对地观测小卫星DMC 4--“北京一号”2005年10月27日在俄罗斯普列谢茨克发射场由宇宙3M火箭成功发射。文章针对小卫星的结构特点和数据特点,指出DMC小卫星图像数据具有获取灵活、重返周期短和大区域覆盖等优势,在地质灾害的调查、监测和预警工作中具有巨大的应用潜力。