Four-body cislunar quasi-periodic orbits and their application to ballistic lunar transfer design

Examining the properties of quasi-periodic orbits provides insight into the Sun-perturbed environment in cislunar space. In this investigation, quasi-periodic trajectories and their properties are explored in the Sun-Earth-Moon four-body problem. Computation and the stability characteristics of families of invariant tori are detailed. Furthermore, this investigation offers a framework for construction of ballistic lunar transfer trajectories in the four-body problem. The framework leverages manifold trajectories to supply a set of initial conditions for construction of periapsis Poincaré maps. Periapsis maps reduce the dimensionality of the space and illuminate solutions of interest as a basis to produce feasible families of transfers. Through a continuation process, families of ballistic transfers and families of transfers that include powered lunar flybys are constructed. Ultimately, these solutions supply an initial guess for transition to the Sun-Earth-Moon ephemeris model.     

地外天体着陆巡视探测自主智能技术进展

以月球、火星和小行星等地外天体着陆巡视探测任务为背景，对自主智能技术的发展现状、应用情况与未来趋势进行了分析研究。回顾了目前国内外地外天体着陆巡视探测任务的实施情况；从导航定位与环境感知、轨迹优化与制导控制、自主探测与路径规划、故障诊断与自主处理等四个方面，阐述了该领域自主智能技术的研究现状，并对未来发展态势进行了展望。     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).     

短波红外全天时自主天文导航技术展望

基于2微米全天巡天（2MASS）短波红外巡天点源星表,分别从白天可探测恒星分布、天空背景辐射与大气传输、传感器性能和光学系统视场大小等方面,分析了短波红外波段相对于可见光波段,在大气层内白天观星实现全天时天文导航的优势,并展望了其发展方向。     

空间低频射电天文天线材质-观测性能分析

15MHz以下的射电天文信号由于受到电离层的阻挡只能通过空间卫星平台进行观测。采用空间展开的电小天线是目前普遍的做法。就电小天线的材质-接收性能方面进行分析，构建了基于材质磁导率、电导率对后端接收机噪声影响模型。通过计算结果比对和就天线材质空间展开难易程度的综合考虑，归纳总结了不同材质对空间低频射电天文观测噪声影响以及展开难度，为新天线的研制提供了数据依据。该项工作将为下一步L1点空间天气监测卫星中的太阳射电观测载荷提供技术支持。