On prediction of re-entry time of an upper stage from GTO

The evolution of objects in geostationary transfer orbit (GTO) is determined by a complex interplay of atmospheric drag and luni-solar gravity. These orbits are highly eccentric (eccentricity >0.7) and have large variations in velocity and perturbations during a revolution. The periodic changes in the perigee altitudes of these orbits are mainly due to the gravitational perturbations of the Sun and the Moon. The re-entry time of the objects in such orbits is sensitive to the initial conditions. The aim of this paper is to study the re-entry time of the cryogenic stage of the Indian geo-synchronous launch vehicle, GSLV-F04/CS, which has been decaying since 2 September 2007 from initial orbit with eccentricity equal to 0.706. Two parameters, initial eccentricity and ballistic coefficient, are chosen for optimal estimation. It is known that the errors are more in eccentricity for the observations based on two line elements (TLEs). These two parameters are computed with response surface method using a genetic algorithm for the selected eight different zones, based on rough linear variation of the mean apogee altitude during 200 days orbit evolution. The study shows that the GSLV-F04/CS will re-enter between 5 December 2010 and 7 January 2011. The methodology is also applied to study the re-entry of six decayed objects (cryogenic stages of GSLV and Molniya satellites). Good agreement is noticed between the actual and the predicted re-entry times. The absolute percentage error in re-entry prediction time for all the six objects is found to be less than 7%. The present methodology is being adopted at Vikram Sarabhai Space Centre (VSSC) to predict the re-entry time of GSLV-F04/CS.     

CCSDS命令与数据获取业务应用分析

针对传统星载数据管理系统应用层与数据访问层耦合度高,应用软件进行设备访问时须了解底层硬件细节、软件复用率低等问题,研究了CCSDS(Consultative Committee for Space Data Systems,空间数据系统咨询委员会)制定的CDAS(Command and Data Acquisition Service,命令与数据获取业务)的功能、特点及其工作原理,结合新一代综合电子系统的硬件模块功能特点,深入分析了如何在综合电子系统中实现CDAS.CDAS已在某预研课题综合电子样机中应用,测试试验表明,CDAS应用于综合电子系统可以为上层应用提供便捷的设备访问服务,为综合电子系统实现任务规划等复杂功能提供有力的技术保障.     

基于知识辅助的海面目标航迹起始方法

针对海环境下不同雷达观测测量准确度存在差异而导致观测数据质量存在差异的问题,提出了一种基于知识辅助的海面目标航迹起始方法。该方法首先利用先验知识幅度信息计算幅度似然比,并以幅度似然比的大小来表示观测数据质量的高低;然后,利用幅度似然比辅助Hough空间参数积累,并对航迹初选参数进行初值修正,提高Mean Shift多尺度聚类滤波初值的准确性,减少迭代次数,再通过最优筛选的方法分析得到目标的真实航迹起始数目和航迹起始参数;最后,通过仿真数据证明了本文所提方法的有效性和优越性。     

锥形螺纹平均螺旋升角的计算

在万工显上,采用影像法测量圆锥形外螺纹的诸参数（例如：牙距、牙形半角等）时,中央显微镜的立柱要倾斜一个角度。与测量普通的圆柱形外螺纹不同的是,该角度是一个平均值,称为平均螺旋升角。给出了平均螺旋升角的三种计算公式及其推导过程。     

一种组合磁钢叠加磁场洛伦兹力磁轴承设计方法

针对现有洛伦兹力磁轴承功耗较高、工作气隙磁场非均匀性问题,提出一种基于组合磁钢叠加磁场的新型洛伦兹力磁轴承设计方法。首先建立了传统洛伦兹力磁轴承电磁力矩模型,揭示了洛伦兹力磁轴承功耗和控制精度与其磁场性能之间的对应关系。在此基础上,引入了磁场强度均值和磁场均匀度的概念,并以此为依据设计出一种轴向整环充磁磁钢与径向分块充磁磁钢组合工作的新型方案。其中轴向充磁磁钢产生主磁场,保证工作气隙周向均匀性,辅助以径向充磁磁钢,使磁场主要聚集在工作气隙处。相比现有洛伦兹力磁轴承,在相同尺寸约束条件下,新型洛伦兹力磁轴承径向磁场均匀度提升了8.7%,磁场强度提升了51.8%,周向磁场连续性显著提高。仿真结果表明,新型洛伦兹力磁轴承可以有效降低功耗、提升控制精度,研究成果可为研制超高精度磁悬浮惯性机构提供一条新的技术途径。     

帧差法和Mean shift算法融合的高速无人机目标跟踪

为实时跟踪高速飞行无人机,图像跟踪算法必须满足快速性和准确性要求。文章给出一个融合算法,将帧差法和 Mean shift算法的优势结合起来。2个算法平行运行,差帧法实现快速跟踪,Mean shift算法则用于对帧差法结果进行准确度修正。还利用 Kalman滤波技术对计算周期内的无人机运动位移进行补偿,进一步提高实时跟踪的准确性,并给出 Matlab仿真例子验证本文方法的有效性。     

一种低轨星座高精度相位保持方法

针对大型低轨星座在轨运行的高精度构型保持问题,提出了一种基于极限环的高精度相位保持方法。首先,推导了实际轨道与参考轨道的平相位角偏差与半长轴偏差的关系；然后,建立了基于极限环的相位保持周期以及半长轴改变量计算方法；最后,基于推导的半长轴偏差与相位偏差的关系,提出了一种相位保持实施方法。考虑地球非球形和大气阻力的数值仿真表明：本文提出的相位保持方法能够在卫星定轨数据精度不高、数据采样间隔较大的情况下,实现低轨星座系统的高精度相位保持。     

航空备件共享与租赁的经济意义

航空备件共享与租赁可削减航空公司的库存费用。持续降低成本的压力及越来越多的航空公司对非核心业务外包做法的认可正激发起人们对此项商业活动的兴趣。     

基于平均轨道要素的干涉SAR编队构形设计方法研究

提出了一种在整个轨道周期内沿轨迹向和垂直轨迹向均存在稳定基线组合的三星编队，并在考虑J2摄动基础上给出了其轨道设计方法。依据系统任务要求给出了该队形设计的约束条件，初步确定了编队的平均轨道参数。为了使得空间基线在摄动情况下保持相对稳定，对卫星平均半长轴进行了小量修正。最后在卫星工具包（STK）下进行了高精度轨道仿真验证。结果表明，该方法设计的编队初始轨道参数能使空间基线保持稳定，在一个轨道周期内，垂直轨迹方向存在两条相同的稳定基线，沿轨迹方向存在一条稳定基线和两条周期性变化基线，能够同时满足DEM和GMTI任务要求。     

Intelligent checking model of Chinese radiotelephony read-backs in civil aviation air traffic control

Federal Aviation Administration (FAA) and NASA technical reports indicate that the misunderstanding in radiotelephony communications is a primary causal factor associated with operation errors, and a sizable proportion of operation errors lead to read-back errors. We introduce deep learning method to solve this problem and propose a new semantic checking model based on Long Short-Time Memory network (LSTM) for intelligent read-back error checking. A mean-pooling layer is added to the traditional LSTM, so as to utilize the information obtained by all the hidden activation vectors, and also to improve the robustness of the semantic vector extracted by LSTM. A MultiLayer Perceptron (MLP) layer, which can maintain the information of different regions in the concatenated vectors obtained by the mean-pooling layer, is applied instead of traditional similarity function in the new model to express the semantic similarity of the read-back pairs quantitatively. The K-Nearest Neighbor (KNN) classifier is used to verify whether the read-back pairs are consistent in semantics according to the output of MLP layer. Extensive experiments are conducted and the results show that the proposed model is more effective and more robust than the traditional checking model to verify the semantic consistency of read-backs automatically.