基于路标观测角的星际着陆器自主位姿确定技术

针对利用导航路标进行六自由度状态估计这一非线性、模糊性问题,对星际着陆器自主位姿确定技术进行了研究。为了减小算法的复杂性,提高求解精度,基于欧式变换下角度不变性,提出以导航路标观测视线之间所形成的夹角作为观测量,对像素观测方程中位置、姿态状态进行解耦求解。通过对观测矩阵的讨论,分析了导航路标空间分布对位姿确定精度的影响,给出了导航路标选取的最优观测方案。最后利用蒙特卡罗仿真对所提导航算法进行了验证,并对影响导航精度的各相关因素进行了分析。     

基于解析梯度的微推进转移轨道优化方法

 研究基于离散脉冲策略的行星际微推进转移轨道优化问题,为改善收敛性和计算效率,提出了一种解析的梯度矩阵计算方法。首先,基于离散脉冲思想建立了行星际微推进转移轨道优化模型,通过对速度脉冲矢量进行参数变换,避免了传统优化模型初值猜测和搜索域难以界定的缺点;然后,基于变分原理分析了离散脉冲模型中轨道状态转移矩阵与状态变分之间的关系,并给出了多种轨道特征条件下状态变分的计算方法,该方法可扩展到多次状态不连续情况;在此基础上,推导了离散脉冲轨道优化模型中性能指标与轨道约束对寻优参数梯度矩阵的解析表达式。以地球-火星的燃料最省微推进转移为例对所提解析梯度方法进行了仿真验证。数值结果表明：本文推导的解析梯度矩阵是正确的,与传统数值梯度计算方法相比,解析梯度矩阵可有效改善寻优算法的收敛特性,并能够提高计算效率约47%。     

Geoeffectiveness of interplanetary shocks controlled by impact angles: A review

The high variability of the Sun’s magnetic field is responsible for the generation of perturbations that propagate throughout the heliosphere. Such disturbances often drive interplanetary shocks in front of their leading regions. Strong shocks transfer momentum and energy into the solar wind ahead of them which in turn enhance the solar wind interaction with magnetic fields in its way. Shocks then eventually strike the Earth’s magnetosphere and trigger a myriad of geomagnetic effects observed not only by spacecraft in space, but also by magnetometers on the ground. Recently, it has been revealed that shocks can show different geoeffectiveness depending closely on the angle of impact. Generally, frontal shocks are more geoeffective than inclined shocks, even if the former are comparatively weaker than the latter. This review is focused on results obtained from modeling and experimental efforts in the last 15?years. Some theoretical and observational background are also provided.     

Forecasting space weather: Predicting interplanetary shocks using neural networks

We are developing a system to predict the arrival of interplanetary (IP) shocks at the Earth. These events are routinely detected by the Electron, Proton, and Alpha Monitor (EPAM) instrument aboard NASA’s ACE spacecraft, which is positioned at Lagrange Point 1 (L1). In this work, we use historical EPAM data to train an IP shock forecasting algorithm. Our approach centers on the observation that these shocks are often preceded by identifiable signatures in the energetic particle intensity data. Using EPAM data, we trained an artificial neural network to predict the time remaining until the shock arrival. After training this algorithm on 37 events, it was able to forecast the arrival time for 19 previously unseen events. The average uncertainty in the prediction 24 h in advance was 8.9 h, while the uncertainty improved to 4.6 h when the event was 12 h away. This system is accessible online, where it provides predictions of shock arrival times using real-time EPAM data.     

Numeric simulations of in situ observations of interacting ejecta near 1 AU

We use a simple numerical model (González-Esparza, J.A., Santillán, A., Ferrer, J. A numerical study of the interaction between two ejecta in the interplanetary medium: one and two dimensional hydrodynamic simulations, Ann. Geophys. 22, 3741–3749, 2004) to study the evolution of three events in the solar wind reported by Wang et al. (Wang, Y.M., Ye, P.Z., Wang, S. Multiple magnetic clouds: several examples during March–April 2001. J. Geophys. Res. 108, 1370, 2003, doi:10.1029/2003JA009850), where two interacting ejecta detected in situ by ACE near 1 AU were related to CMEs observed previously by SOHO-LASCO. The study is based on a 1-D hydrodynamic model using the ZEUS code (Stone, J.M., Norman, M. ZEUS 2-D: A radiation magnetohydrodynamics code for astrophysical flows in two dimensions, I, the hydrodynamics algorithms and tests, Astrophys. J. 80, 753, 1992). Although this model cannot address either the magnetic field dynamics or the complex geometrical effects intrinsic in the three-dimensional nature of the phenomena, it illuminates the transferring of momentum and evolution of interacting large-scale solar wind disturbances in those cases where there is no merging (magnetic reconnection) between the two ejecta. This model can reproduce, in some cases, characteristics of the events such as transit times and flow signatures as inferred from the two-point measurements from spacecraft.