落儿岭-土地岭断裂几何结构及晚第四纪活动特征初探

落儿岭-土地岭断裂是东大别地区重要的发震构造,然而限于自然地理条件等因素,前人对其的研究并不充分。本文以发生多次中强地震的落儿岭-土地岭断裂为研究对象,在高精度卫星影像解译的基础上,通过详细的野外地质地貌调查,尤其是对典型断层剖面进行分析,研究落儿岭-土地岭断裂的断错地质地貌特征、几何结构及活动特征。通过野外调查并结合其他资料分析认为落儿岭-土地岭断裂为发育于大别造山带内部的一条走向NE、向NW陡倾的断裂带。依据地质地貌特征及地震活动性,断裂可以划分为杨树沟-黑石渡段和黑石渡-横塘岗乡段两个几何段落。断裂最新活动时代为中更新世晚期—晚更新世早期,断裂最新活动继承了中生代以来的运动方式,以兼具右旋走滑的拉张正断为主。     

整体构件周向叶片电解加工流场设计及实验

针对整体构件周向叶片的特点与加工难点,设计了带有内置绝缘腔的电解套型加工工具阴极,建立了两种进液方式的流场三维模型,采用有限元法对两种进液方式的流场进行仿真计算。计算结果表明由绝缘腔的叶盆叶背两侧进液并对出液口进行优化的流场方式较好,电解液流速较高且流场较均匀。使用根据有限元仿真结果设计的阴极开展实验,结果表明实验与流场仿真结果相符,采用优化流场模型的加工过程流场稳定,加工效率较高。     

A Test Method for the Static/Moving State of Targets Applied to Airport Surface Surveillance MLAT System

Due to the particularity of its location algorithm,there are some unique difficulties and features regarding the test of target motion states of multilateration(MLAT)system for airport surface surveillance.This paper proposed a test method applicable for the airport surface surveillance MLAT system,which can effectively determine whether the target is static or moving at a certain speed.Via a normalized test statistic designed in the sliding data window,the proposed method not only eliminates the impact of geometry Dilution of precision(GDOP)effectively,but also transforms the test of different motion states into the test of different probability density functions.Meanwhile,by adjusting the size of the sliding window,it can fulfill different test performance requirements.The method was developed through strict theoretical extrapolation and performance analysis,and simulations results verified its correctness and effectiveness.     

基于刚体自由转动的连续推力消旋方法研究

针对空间失效卫星或废弃碎片转速过快,难以对其直接处理的问题,提出了一种基于刚体转动的连续小推力消旋方法。分析了轴对称消旋目标的运动特性,设计了消旋方法。将轴对称消旋目标的消旋方法推广到一般的消旋目标上,通过变换欧拉方程得到消旋目标各角速度分量的变化趋势和最终状态。通过一般消旋目标的仿真实验验证了该方法的可行性。结果表明:按给定顺序和方向施加单轴外力矩,能够有效降低消旋目标的转速。区别于传统的控制方法,最优控制方法需要精确测量消旋目标的实时状态信息,而分步消旋方法只需分辨角速度分量的正负,且控制力矩只需反向变化,推力器无需摆动,因此降低了对控制系统的要求。     

Lunar geophysical parameters inversion based on gravity/topography admittance and particle swarm optimization

This paper presents a FORTRAN computer program. The program as code will be used for lunar parameter inversions based on gravity/topography admittance. This will be done by assuming that the lunar lithosphere is modeled as a thin elastic spherical shell. The parameters discussed here include; load ratio, crustal thickness, subsurface load depth, crustal density and elastic lithosphere thickness. The admittance of the best-fitting model can be found through automatically adjusting misfits between one theoretical admittance and an observed one. The results in this paper indicate that this research’s theoretical model is reasonable for exploring the best-fitting parameters. In addition, this code is not only able to automatically and simultaneously calculate the global optimum solution of the parameters studied, but also performs well in computational speed. The code can be easily modified to include more parameter inversions; such as the inversion for subsurface density anomaly and the case of considering infilling material in some lunar mare basins.     

Accuracy analysis of GNSS-IR snow depth inversion algorithms

In recent years, with the continuous development of Global Navigation Satellite System (GNSS), it has been applied not only to navigation and positioning, but also to Earth surface environment monitoring. At present, when performing GNSS-IR (GNSS Interferometric Reflectometry) snow depth inversion, Lomb-Scargle Periodogram (LSP) spectrum analysis is mainly used to calculate the vertical height from the antenna phase center to the reflection surface. However, it has the problem of low identification of power spectrum analysis, which may lead to frequency leakage. Therefore, Fast Fourier Transform (FFT) spectrum analysis and Nonlinear Least Square Fitting (NLSF) are introduced to calculate the vertical height in this paper. The GNSS-IR snow depth inversion experiment is carried out by using the observation data of P351 station in PBO (Plate Boundary Observatory) network of the United States from 2013 to 2016. Three algorithms are used to invert the snow depth and compared with the actual snow depth provided by the station 490 in the SNOTEL network. The observations data of L1 and L2 bands are respectively used to find the optimal combination between different algorithms further to improve the accuracy of GNSS-IR snow depth inversion. For L1 band, different snow depths correspond to different optimal algorithms. When the snow depth is less than 0.8 m, the inversion accuracy of NLSF algorithm is the highest. When the snow depth is greater than 0.8 m, the inversion accuracy of FFT algorithm is higher. Therefore, according to the different snow depth, a combined algorithm of NLSF + FFT is proposed for GNSS-IR snow depth inversion. Compared with the traditional LSP algorithm, the inversion accuracy of the combined algorithm is improved by 10%. For L2 band data, the results show that the accuracy of snow depth inversion of various algorithms do not change with the variations of snow depth. Among the three single algorithms, the inversion accuracy of FFT algorithm is better than that of LSP and NLSF algorithms.     

广义轨道根数空间的轨道机动可达性

在由动量矩矢量和偏心率矢量定义的广义轨道根数度量空间内,研究了在初始轨道的任意点施加幅值固定的单脉冲后卫星的可达区域,包括脉冲施加的位置任意、方向固定和位置固定、方向任意2种情况,给出了广义轨道根数空间中轨道机动的可达性判据.通过比较脉冲作用前后的广义轨道根数,评估了单脉冲对广义轨道根数的影响,并且利用数值仿真分析了脉冲作用位置、方向与广义轨道根数空间中度量的关系.最后,在广义轨道根数空间中给出了轨道到轨道的机动策略,验证了广义轨道根数的可行性.     

Improving orbit prediction accuracy through supervised machine learning

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs’ trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO’s orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.     

Co-registration of Chang’E-1 stereo images and laser altimeter data with crossover adjustment and image sensor model refinement

Topographic information is of fundamental importance for various scientific investigations in lunar and planetary exploration. To provide high-precision, seamless mapping capability, it is critical to co-register image and altimetry data, the two major data sources for topographic modeling. This paper presents a method for co-registration of Chang’E-1 (CE-1) stereo images and laser altimeter (LAM) data with crossover adjustment and refinement of the image sensor model. The crossover adjustment is tested in a larger area (0–60°N, 50–0°W); the image refinement and co-registration with LAM data are tested in an area (46.2–50.0°N, 31.8–28.8°W) within the larger area. Experimental results demonstrate that this co-registration reduces the mean differences of inconsistency from more than 200 m to just 3.21 m in the Z direction of object space. In image space, the mean errors of homologous points both in the column and row directions are reduced to below 0.1 pixel. This indicates that the proposed crossover adjustment of LAM data and refinement of the CE-1 stereo image model can effectively improve co-registration of the two data sets.