基于最小轨道交叉距离的小行星顺访探测目标预筛选方法

小行星探测有助于研究太阳系演化等重要科学问题,在深空探测任务转移途中实施小行星顺访探测可增加科学研究回报。直接通过轨道递推筛选小行星探测目标计算量大、效率低,针对该问题提出了基于最小轨道交叉距离的目标预筛选方法。在推导出适用于计算双曲线轨道的最小轨道交叉距离公式后,将此理论应用到小行星顺访探测目标筛选中。首先基于探测器与小行星轨道的形状、空间位置计算二者轨道在空间中的几何最近距离,预筛选出可能满足接近距离标准的小行星目标;然后基于轨道递推模型,筛选出真实最近距离小于可接近标准的目标小行星。仿真结果显示,基于最小轨道交叉距离的预筛选方法可有效减少计算量,降低计算时间,提高小行星顺访目标筛选的效率。      

Uncertainty-aware Cube algorithm for medium-term collision risk assessment

The number of Earth orbiting objects is constantly growing, and some orbital regions are becoming risky environments for space assets of interest, which are increasingly threatened by accidental collisions with other objects, especially in Low-Earth Orbit (LEO). Collision risk assessment is performed by various methods, both covariance and non-covariance based. The Cube algorithm is a non-covariance-based method used to estimate the collision rates between space objects, whose concept consists in dividing the space in cubes of fixed dimension and, at each time instant, checking if two or more objects share the same cube. Up to now its application has been limited to the long-term scenarios of orbital debris evolutionary models, where considering the uncertainties is not necessary and impractical. Within operative contexts, instead, medium-term collision risk analysis may be an important task, in which the propagation-related uncertainties play a prominent role, but the timescale poses challenges for the application of standard covariance-based conjunction analysis techniques. In this framework, this paper presents an approach for the evaluation of the medium-term collision frequency for objects in LEO, called Uncertainty-aware Cube method. It is a modified version of the Cube, able to take the possible errors in the space objects’ position into account for the detection of the conjunctions. As an object’s orbit is propagated, the along-track position error grows more and more, and each object could potentially be in a different position with respect to the one determined by numerical propagation and, thus, in a different cube. Considering the uncertainties, at each time instant the algorithm associates more than one cube to each object and checks if they share at least one cube. If so, a conjunction is detected and a degree of confidence is evaluated. The performance of the method is assessed in different LEO scenarios and compared to the original Cube method.     

基于随机点模拟方法分析空间目标危险交会

基于SGP4模型在空间目标轨道预报中的应用, 在预报的位置速度信息和误差 信息基础上, 提出一种空间两目标碰撞预警的分析方法, 即随机点模拟方法. 与传统的交会平面积分方法相比, 其主要有两点不同: 一是在误差信息中考虑 了误差均值的影响, 即误差椭球不再以预报位置为中心分布; 二是在分析方 法上侧重于真实模拟可能的交会情形, 而不忽略任一方向上的误差. 通过算例分 析验证了该方法的可行性, 结果表明误差均值的非零性使得最大碰撞概率不 一定出现在预报的最近交会距离时刻. 同时仿真结果还表明, 两目标在相对速 度方向上的相对位置仍然存在误差, 这可能造成随机点模拟的碰撞概率计算 值较交会平面积分方法偏小. 不同的碰撞预警分析方法对应不同的预警门限, 根据文中实例, 初步确定10-6为随机点模拟方法的红色预警值.      

ITRS/GCRS transformation: Uncertainty propagation analysis and short-term modelling of IAU 2006/2000A developments

A study of the uncertainty propagation in ITRS/GCRS transformation is presented in this work. General law of propagation of variances is applied to the ITRS/GCRS transformation matrix, deriving the analytical expressions to compute GCRS position uncertainty. This evaluation is based on EOP uncertainties provided by IERS long-term series and formal uncertainties of ITRS-compatible coordinates. Numerical results for the period 1998–2016 are shown and discussed for ITRS positions in different altitudes and latitudes, providing graphical and numerical insights of the mapping of EOP uncertainties to transformed coordinates.Eventually, an analysis of short-term evolution of the Celestial Intermediate Pole coordinates in the GCRS provided by the IAU2006/2000A precession-nutation model is carried out, in order to assess the feasibility to potentially broadcast these parameters in GNSS navigation message. This approach would facilitate the dissemination of terrestrial-celestial transformation parameters for real time users, given that polar motion and UT1-UTC are already foreseen in GPS interface specification. The results presented in this work confirm the feasibility of this idea.     

An optimization approach for observation association with systemic uncertainty applied to electro-optical systems

The observation to observation measurement association problem for dynamical systems can be addressed by determining if the uncertain admissible regions produced from each observation have one or more points of intersection in state space. An observation association method is developed which uses an optimization based approach to identify local Mahalanobis distance minima in state space between two uncertain admissible regions. A binary hypothesis test with a selected false alarm rate is used to assess the probability that an intersection exists at the point(s) of minimum distance. The systemic uncertainties, such as measurement uncertainties, timing errors, and other parameter errors, define a distribution about a state estimate located at the local Mahalanobis distance minima. If local minima do not exist, then the observations are not associated. The proposed method utilizes an optimization approach defined on a reduced dimension state space to reduce the computational load of the algorithm. The efficacy and efficiency of the proposed method is demonstrated on observation data collected from the Georgia Tech Space Object Research Telescope.     

可容忍信标误差的三维传感网节点定位方法

针对信标位置存在误差情况下的三维无线传感器网络节点定位问题,提出一种基于正交回归的多跳定位方法.同时考虑到自变量误差和因变量偏差对节点坐标估计的影响,基于约束加权正交回归参数估计准则,建立可容忍信标位置误差的三维多跳定位模型,解决了信标位置和距离估计两方面的误差并存时的节点自定位问题,并给出求解节点坐标最优值的数值方法;推导出相应的坐标估计精度评估标准3D-MCRB (3D Multi-hop Cramér-Rao Bound).仿真结果表明:此方法对信标位置误差和距离估计误差都具有较好的抑制能力,在大多数实验条件下,能将定位精度提高10%以上.     

Explicit expression of collision probability in terms of RSW and NTW components of relative position

The existing analytical and numerical computing methods for collision probability (P_c) provide sufficiently accurate results, but do not reveal the direct connection between P_c and conjunction geometry or error covariance. This paper derived an explicit expression of P_c under the circular orbit assumption, based on analysis of closest approach distance and Chan’s analytical formulae for P_c. The P_c was expressed as an explicit function of the radial, in-track, cross-track components of relative position and error covariance. The explicit expression is simple and compact, relates P_c with components of relative position, which are two kinds of important conjunction risk criteria. By introducing non-zero flight-path angle and ratio of velocity’s magnitude, a modified expression of P_c was presented for objects in eccentric orbits. For objects with eccentricity less than 0.01 (which account for 62% of all LEO objects), the relative error of the explicit expression is less than 0.107, or 0.252 for typical conjunction cases. What really matters in conjunction risk assessment is the order of magnitude rather than the specific value of P_c, the precision of the explicit expression is sufficient for conjunction risk assessment and decision-making for most LEO objects.     

可重复使用运载器递归积分滑模姿态控制方法

针对存在模型不确定和外部干扰的可重复使用运载器再入段姿态控制问题,提出了一种基于自适应滑模干扰观测器的递归积分滑模控制方法。首先,基于可重复使用运载器再入段姿态运动模型,建立了面向控制的模型;其次,设计了自适应滑模干扰观测器,以精准估计和补偿由模型不确定和外部干扰构成的复合干扰;然后,基于递归思想设计了一种新型递归积分滑模控制器,利用Lyapunov稳定性理论证明了闭环系统的有限时间稳定性;最后,数值仿真结果验证了该方法具有较强的鲁棒性和较快的收敛速度。     

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).     

基于特征模型的高阶线性不稳定系统的参数辨识与控制

在仅已知系统相对阶的情况下,通过串联差分器(微分器)的方法使系统的相对阶条件得到满足,从而使基于二阶特征模型的全系数自适应控制可以应用于高相对阶的最小相位高阶线性不稳定对象.针对测量存在噪声的问题,又提出一种改进的强跟踪滤波方法对测量信号进行去噪声处理,从而显著地改善了控制量和被控对象输出的性能.最后,通过对几种控制方案的控制性能比较,说明所提出的基于串联差分器和改进的强跟踪滤波相结合的全系数自适应控制方案可以较好地处理含测量噪声情况下的高相对阶系统的镇定控制问题.