面向太阳系边际探测的激光通信方案研究

针对我国太阳系边际探测任务需求,提出了利用激光链路进行对地高速信息传输的方案。梳理了深空激光通信的国外发展现状,分析了太阳系边际探测任务的激光链路特点和约束条件,给出了飞行激光终端和激光地面站的初步方案和主要参数,进行了链路预算和影响因素分析,并对我国未来太阳系边际探测任务激光通信的发展进行展望,为我国太阳系边际探测任务的激光通信论证与实施提供参考。     

考虑火箭约束的深空探测弹轨道拼接模型

针对深空探测任务轨迹规划中对于运载火箭弹道约束考虑不足、弹轨道拼接设计效率低的现状,建立了考虑运载火箭射向和末级滑行时间约束的弹轨道拼接计算模型,在无引力摄动假设下得到了任意深空出发速度条件下的运载火箭射向和末级滑行时间计算公式。利用该模型分析了弹轨道可成功拼接的集合范围随深空出发速度、发射场地理位置、火箭射向和滑行时间约束范围的变化规律。提出深空出发"赤纬-发射能量"(δ-C3)图方法,可在图上表示指定型号运载火箭深空发射能力可行域,结合深空轨道转移Pork-Chop图方法,可以快速判断该型运载火箭是否适用于特定深空发射任务。依据这一方法,提出了对我国未来深空探测运载火箭射向和末级滑行时间的能力需求。     

卫星介质深层充电的两类计算模型对比分析

计算模拟是评估航天器介质深层充电危害的重要研究方法之一. 通过粒子输运 模拟, 可以得到特定空间辐射环境下介质中的电荷沉积分布, 进而根据电位/电 场计算模型, 得到深层充电结果. 前期研究多是围绕RIC (辐射诱导电导率)模 型及其改进模型展开的, 而目前通常采用基于电流守恒定律的简单计算模型. 为了研究二者关系, 给出其各自求解方法, 并采用已发表数据对计算结果进行 验证; 从理论上阐述了后者是RIC模型的进一步简化, 只要二者考虑相同的介 质电导率, 则对应计算结果就是一致的; 结合GEO恶劣电子辐射环境下平板介 质模型在三类边界条件下的充电情况, 进行了充分的仿真验证. 相关结论为介 质深层充电效应评估提供了有益参考.     

航天器介质放电脉冲频谱特征实验研究

空间电子辐射环境下,航天器介质的充放电效应是威胁航天器安全的重要因素.介质放电现象除与材料参数及构型相关外,还与空间电子环境密切相关.本文通过电子枪和Sr⁹⁰放射源在地面实验装置上模拟空间电子辐照环境,测试了环氧树脂、聚四氟乙烯、聚酰亚胺等常见空间材料在不同温度、不同电子能量和电子束流强度影响下的放电脉冲,并对放电电流脉冲和电场脉冲进行频谱分析.实验分析结果表明,介质材料的放电电流脉冲频谱具有明显的单峰结构,该峰值与材料厚度和入射电子能量相关,但受材料温度和辐照束流强度影响不大.     

Development of New Capabilities Using Machine Learning for Space Weather Prediction

With the development of space exploration and space environment measurements, the numerous observations of solar, solar wind, and near Earth space environment have been obtained in last 20 years. The accumulation of multiple data makes it possible to better use machine learning technique, which has achieved unforeseen results in industrial applications in last decades, for developing new approaches and models in space weather investigation and prediction. In this paper, the efforts on the forecasting methods for space weather indices, events, and parameters using machine learning are briefly introduced based on the study works in recent years. These investigations indicate that machine learning, especially deep learning technique can be used in automatic characteristic identification, solar eruption prediction, space weather forecasting for solar and geomagnetic indices, and modeling of space environment parameters.     

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

A method of multi-block Single Shot Multi Box 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...     

Imaginary filtered hindsight experience replay for UAV tracking dynamic targets in large-scale unknown environments

As an advanced combat weapon, Unmanned Aerial Vehicles (UAVs) have been widely used in military wars. In this paper, we formulated the Autonomous Navigation Control (ANC) problem of UAVs as a Markov Decision Process (MDP) and proposed a novel Deep Reinforcement Learning (DRL) method to allow UAVs to perform dynamic target tracking tasks in large-scale unknown environments. To solve the problem of limited training experience, the proposed Imaginary Filtered Hindsight Experience Replay (IFHER) generates successful episodes by reasonably imagining the target trajectory in the failed episode to augment the experiences. The well-designed goal, episode, and quality filtering strategies ensure that only high-quality augmented experiences can be stored, while the sampling filtering strategy of IFHER ensures that these stored augmented experiences can be fully learned according to their high priorities. By training in a complex environment constructed based on the parameters of a real UAV, the proposed IFHER algorithm improves the convergence speed by 28.99% and the convergence result by 11.57% compared to the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the IFHER agent. Moreover, the flight trajectory of the IFHER agent shows the superiority of the learned policy and the practical application value of the algorithm.     

宇航电绝缘材料深空环境适应性研究

宇航电绝缘材料是深空探测器电信号传递和作用的界限，评估宇航电绝缘材料的环境适应性是材料选择，绝缘设计及工艺处理过程中需要考虑和优化的项目。本文从材料电绝缘性能关联的环境因素入手，介绍了深空辐射环境、热环境、气体环境及颗粒物环境及其效应，给出了深空环境适应性评估流程及验证项目及方法，为宇航电绝缘材料的选用和评估提供参考。     

一种无人机自主避障与目标追踪方法

针对无人机自主避障与目标追踪问题，以深度Q网络(DQN)算法为基础，提出一种多经验池深度Q网络(MP DQN)算法，使无人机避障与追踪的成功率和算法的收敛性得到优化。更进一步，赋予无人机环境感知能力，并在奖励函数中设计了方向奖惩函数，提升了无人机对环境的泛化能力以及算法的整体性能。仿真结果表明，相较于DQN和双重DQN(DDQN)算法，MP DQN算法具有更快的收敛速度、更短的追踪路径和更强的环境适应性。     

Force measurement using strain-gauge balance in shock tunnel based on deep learning

When a force test is conducted in a shock tunnel, vibration of the Force Measurement System (FMS) is excited under the strong flow impact, and it cannot be attenuated rapidly within the extremely short test duration of milliseconds order. The output signal of the force balance is coupled with the aerodynamic force and the inertial vibration. This interference can result in inaccurate force measurements, which can negatively impact the accuracy of the test results. To eliminate inertial vibration interference from the output signal, proposed here is a dynamic calibration modeling method for an FMS based on deep learning. The signal is processed using an intelligent Recurrent Neural Network (RNN) model in the time domain and an intelligent Convolutional Neural Network (CNN) model in the frequency domain. Results processed with the intelligent models show that the inertial vibration characteristics of the FMS can be identified efficiently and its main frequency is about 380 Hz. After processed by the intelligent models, the inertial vibration is mostly eliminated from the output signal. Also, the data processing results are subjected to error analysis. The relative error of each component is about 1%, which verifies that the modeling method based on deep learning has considerable engineering application value in data processing for pulse-type strain-gauge balances. Overall, the proposed dynamic calibration modeling method has the potential to improve the accuracy and reliability of force measurements in shock tunnel tests, which could have significant implications for the field of aerospace engineering.