基于LEO辅助的北斗B1C高灵敏快速捕获算法

随着低轨(LEO)卫星数量的不断增加,利用LEO星座辅助增强GNSS导航性能已经成为一种新的趋势.针对低信噪比环境下B1C信号难以捕获的问题,提出了一种基于LEO辅助的B1C信号高灵敏快速捕获算法.首先对提升接收机捕获灵敏度进行了分析,对比了相干积分与非相干积分对于信号处理增益的影响,得出在低轨导航增强信号的辅助下采用增加相干积分时间的捕获算法对低信噪比条件下B1C信号的捕获更有效.然后提出了一种基于LEO辅助的B1C高灵敏快速捕获算法,从理论分析和实验仿真两方面,对比验证了在LEO辅助下可以显著提高B1C信号的捕获灵敏度,缩短捕获时间,提高捕获效率.     

An atmosphere-breathing propulsion system using inductively coupled plasma source

CubeSats have attracted more research interest recently due to their lower cost and shorter production time. A promising technology for CubeSat application is atmosphere-breathing electric propulsion, which can capture the atmospheric particles as propulsion propellant to maintain long-term mission at very low Earth orbit. This paper designs an atmosphere-breathing electric propulsion system for a 3 U CubeSat, which consists of an intake device and an electric thruster based on the inductively coupled plasma. The capture performance of intake device is optimized considering both particles capture efficiency and compression ratio. The plasma source is also analyzed by experiment and simulation. Then, the thrust performance is also estimated when taking into account the intake performance. The results show that it is feasible to use atmosphere-breathing electric propulsion technology for CubeSats to compensate for aerodynamic drag at lower Earth orbit.     

Land use and cover change (LUCC) impacts on Earth’s eco-environments: Research progress and prospects

Land use and cover change (LUCC) is one of the key variables dominating land–atmosphere interactions and strongly affects the Earth’s eco-environments by altering surface properties. Numerous studies have been carried out to assess the impact of LUCC. However, the Earth is a large, open and complex system characterized by complex interactions between its eco-environments and drivers. This study aimed to summarize previous studies of the impact of LUCC on the Earth’s eco-environments and discuss the progress and limitations in suggesting future directions. Previous studies have confirmed that LUCC has a wide range of impacts on the Earth’s eco-environments, which are represented by the alternation of climate (temperature, precipitation, wind, and humidity), hydrology (soil moisture, runoff, and evapotranspiration), ecology and environmental (air, water, and soil) pollution. Physically, the impacts were mainly attributed to the disturbance of the surface radiation budget and matter conservation caused by LUCC. Although great achievements have been made, several challenges remain because of the unavoidable uncertainties in data sources and methodologies and the complexity of eco-environmental evolution. Therefore, data assimilation, physical-based investigations, contribution isolation, and full-process analysis are required to overcome these challenges in future research. The results of this study helped to capture the impact of LUCC and its physical mechanisms, which provide useful clues for future research and support the relative land use management for sustainable development.     

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).