基于优先级和率失真模型的CCSDS-IDC码率分配算法

为了解决遥感图像采集与空间网络传输能力之间的矛盾,迫切需要具有码率控制功能的图像压缩算法。文章面向军事目标监测领域图像目标区域局部高质量需求,针对CCSDS-IDC（TheConsultativeCommitteeforSpaceDataSystems ImageDataCompressionStandard）图像压缩标准,提出了一种码率分配算法。新算法改进了原算法的分段方式,设置了段优先级,并根据优先段覆盖的比例及率失真模型设计了码率分配方案。结果表明新算法在局部峰值信噪比和局部视觉效果上较原算法有明显的提高,当优先段覆盖率低于压缩比倒数时,新算法的整体峰值信噪比与原算法接近。     

遥感卫星高速数传信息流设计

随着卫星遥感技术水平的提高,遥感数据的类型和数据量快速增加。为适应多类型、高速率遥感数据传输的复杂需求,对数传信息流进行了顶层设计,定义了数传与遥感系统数据接口以及数传帧格式,对遥感数据传输所需码速率进行了分类计算,为设计固定的下行数传码速率提供了依据。进而针对不同类型的遥感数据提出了基于分组优先级虚拟信道动态调度策略的数传信息流设计方案,确保不同类型遥感数据的传输满足不同的应用需求。对高速遥感数据确保满足较低的缓存容量需求,对低速遥感数据确保满足实时性传输需求。采用动态仿真技术对数传信息流设计方案进行了试验验证。设计方案可为后续新一代遥感卫星数传系统设计提供参考。     

使用解析目标分流策略的海洋卫星多学科优化

在海洋卫星总体设计阶段,为了提高卫星整体设计性能和效率,采用多学科设计优化（MDO）技术对其总体参数进行设计优化。考虑轨道、有效载荷、结构和电源四个学科的设计变量和约束条件,以卫星总质量最小为优化目标建立海洋卫星MDO模型;针对各学科优化问题属性设计高效的优化方案并采用解析目标分流（ATC）这一多级MDO策略来协调整个优化过程。优化后海洋卫星总质量相对海洋一号（HY-1）卫星下降17.6%,整个优化过程花费的时间也比采用单级All at once(AAO)策略减少85.7%。一方面表明所建立的海洋卫星MDO模型的合理有效性,另一方面表明ATC策略结合学科定制优化方案的求解技术的有效性。     

基于求解校验序列的(n,k,m)卷积码盲识别

针对卫星通信和深空探测等采用的(n,k,m)卷积码盲识别问题,提出了一种新的识别方法。首先,根据卷积码本身的线性约束关系改进分析矩阵模型的构造方法,有效地减小了识别所需数据量。在通过矩阵化简识别出码长、码字起始位置和校验序列基础上,提出了由校验序列推导出生成多项式矩阵的方法,进而完成卷积码的识别。仿真实例表明,该识别方法不需要任何先验条件,能够有效识别所有(n,k,m)卷积码。     

Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

22 Years of AJISAI spin period determination from standard SLR and kHz SLR data

Satellite Laser Ranging (SLR) is a powerful and efficient technique to measure spin parameters of satellites equipped with corner cube reflectors. We obtained spin period determination of the satellite AJISAI from SLR data only: 17246 pass-by-pass estimates from standard 1–15 Hz SLR data (14/Aug/1986–30/Dec/2008) and 1444 pass-by-pass estimates (9/Oct/2003–30/Dec/2008) from data of the first 2 kHz SLR system from Graz, Austria. A continuous history of the slowing down of AJISAI spin is derived from frequency analysis, and corrected for the apparent effects. The apparent corrections, elaborated here, allowed very accurate determination of AJISAI initial spin period: 1.4855 ± 0.0007 [s]. The paper identifies also non-gravitational effects as a source of the periodical changes in the rate of slowing down of the satellite.     

编队卫星防碰撞规避路径规划方法与控制研究

针对编队卫星在构型重构和构型失效重组时发生碰撞可能性最大的两种机动工况,采用数学规划的方法对其进行防碰撞规避准则、规避策略和控制方面的研究;并通过系统可控性证明出无径向推力相对于全向推力,也可以达到目标相位点只是燃料消耗得多。对编队构型失效重组工况中备份星机动前停泊位置,进入编队位置选择和启动时刻选择以及故障星离开编队后,目标轨道确定等问题通过数学仿真的形式予以解答。仿真结果表明,该研究方法真实可信、简便有效。     

卫星编队飞行的相对轨道自主确定研究

为了研究卫星编队飞行相对轨道的自主确定,基于相对轨道根数建立编队卫星间的相对运动方程,利用测量所得到的星间距离和方位信息作为观测量。不同于目前广泛采用的扩展卡尔曼滤波算法,设计Unscented Kalman Filter(UKF)算法实现卫星编队飞行的相对轨道自主确定。仿真结果表明这种相对轨道自主确定方案能获得较高的定轨精度。     

Earth remote sensing as an effective tool for the development of advanced innovative educational technologies

Current educational system is facing a contradiction between the fundamentality of engineering education and the necessity of applied learning extension, which requires new methods of training to combine both academic and practical knowledge in balance. As a result there are a number of innovations being developed and implemented into the process of education aimed at optimizing the quality of the entire educational system. Among a wide range of innovative educational technologies there is an especially important subset of educational technologies which involve learning through hands-on scientific and technical projects. The purpose of this paper is to describe the implementation of educational technologies based on small satellites development as well as the usage of Earth remote sensing data acquired from these satellites. The increase in public attention to the education through Earth remote sensing is based on the concern that although there is a great progress in the development of new methods of Earth imagery and remote sensing data acquisition there is still a big question remaining open on practical applications of this kind of data. It is important to develop the new way of thinking for the new generation of people so they understand that they are the masters of their own planet and they are responsible for its state. They should desire and should be able to use a powerful set of tools based on modern and perspective Earth remote sensing. For example NASA sponsors “Classroom of the Future” project. The Universities Space Research Association in United States provides a mechanism through which US universities can cooperate effectively with one another, with the government, and with other organizations to further space science and technology, and to promote education in these areas. It also aims at understanding the Earth as a system and promoting the role of humankind in the destiny of their own planet. The Association has founded a Journal of Earth System Science Education. Authors describe an effective model of educational technology developed in the Center for Earth Remote Sensing of Bauman Moscow State Technical University and based on scientific and educational organizations integration in the field of applied studies. The paper also presents how students are being trained to acquire and process satellite imagery data from Terra and Aqua satellites. It also reveals the results of space monitoring for Russia's ecologically complex regions conducted by Bauman Moscow State Technical University students in cooperation with specialists from the Laboratory for Aerospace Methods of Moscow State University named after M. Lomonosov.