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。     

飞船返回舱红外跟踪

提出一种联合大底检测的改进的Mean-Shift（均值移位）算法,将其应用于返回舱红外跟踪。新算法采用非均匀量化加权直方图构造目标特征矢量,以提高目标描述的准确性。为避免跟踪到大底上去,使用＂滑窗式＂大底检测算法实时修正Mean-Shift跟踪坐标。通过对飞船返回实况判读验证,提出的新算法处理速度达到20帧/s以上,并且能够可靠和准确地跟踪返回舱。     

Towards development of a consistent orbit series for TOPEX,Jason-1, and Jason-2

The TOPEX/Poseidon, Jason-1 and Jason-2 set of altimeter data now provide a time series of synoptic observations of the ocean that span nearly 17 years from the launch of TOPEX in 1992. The analysis of the altimeter data including the use of altimetry to monitor the global change in mean sea level requires a stable, accurate, and consistent orbit reference over the entire time span. In this paper, we describe the recomputation of a time series of orbits that rely on a consistent set of reference frames and geophysical models. The recomputed orbits adhere to the IERS 2003 standards for ocean and earth tides, use updates to the ITRF2005 reference frame for both the SLR and DORIS stations, apply GRACE-derived models for modeling of the static and time-variable gravity, implement the University College London (UCL) radiation pressure model for Jason-1, use improved troposphere modeling for the DORIS data, and apply the GOT4.7 ocean tide model for both dynamical ocean tide modeling and for ocean loading. The new TOPEX orbits have a mean SLR fit of 1.79 cm compared to 2.21 cm for the MGDR-B orbits. These new TOPEX orbits agree radially with independent SLR/crossover orbits at 0.70 cm RMS, and the orbit accuracy is estimated at 1.5–2.0 cm RMS over the entire TOPEX time series. The recomputed Jason-1 orbits agree radially with the Jason-1 GDR-C orbits at 1.08 cm RMS. The GSFC SLR/DORIS dynamic and reduced-dynamic orbits for Jason-2 agree radially with independent orbits from the CNES and JPL at 0.70–1.06 cm RMS. Applying these new orbits, and using the latest altimeter corrections for TOPEX, Jason-1, and Jason-2 from September 1992 to May 2009, we find a global rate in mean sea level of 3.0 ± 0.4 mm/yr.     

预测-校正EKF算法在自主导航中的应用

为提高航天器自主导航系统非线性估计精度,通过分析EKF线性化方法的缺 点,基于数学分析理论研究了非线性函数线性化展开点以及雅可比矩阵取值点不同对线性化 逼近程度的影响,然后对传统EKF算法的线性化展开方式进行了改进,提出了一种基于中值 定理和平滑估计的预测-校正EKF（FR\|EKF）新算法。仿真结果表明,该算法在精度上优于 EKF算法。     

一种轨道式动态目标跟踪算法

研究了一种轨道式动态目标跟踪的算法,建立轨迹式目标跟踪模型;分析了目标跟踪的基本原理,研究跟踪平台的光学结构和转台角度与轨道目标运动的跟踪算法,给出了物像动态跟踪的计算方法;研究Mean shift跟踪算法,利用它的核密度估计和跟踪图像的帧图像关系,确定目标检索中心位置,以达到动态目标跟踪目的。通过试验验证,提出的算法在外弹道轨道目标动态跟踪系统中是可行的。     

OFDM系统中TURBO码译码算法研究

研究多径衰落信道下TURBO编码OFDM系统的特性,推导出一种TURBO码在OFDM系统具体应用方式,得到OFDM系统中TURBO的译码算法。并从推导结果确定,平均信噪比和信噪比弥散度是影响系统性能的两个重要参数。仿真结果表明,与IEEE 802.11 A定义的物理层性能相比,采用提出的TURBO译码算法,可以提高整个系统性能,信噪比改善2.9DB;在大平均信噪比和小信噪比弥散度条件下,系统性能较好,误帧率较低。     

基于Kalman滤波的卫星光谱成像仪非均匀性场景校正

为消除在轨卫星光谱成像仪的非均匀性,提出了一种基于Kalman滤波器校正算法.给出了校正算法,建立了均方根误差和列向粗糙度等评价指标.对美国TERRA卫星MODIS仪器图像的校正试验结果表明:用Kalman滤波器校正方法去除遥感图像中非均匀性条带的效果明显,校正后图像的品质显著提高.     

一种车窗定位算法的设计和实现

利用均值色度函数，结合提色块算法，对车窗定位的算法进行了研究，提出了一种均值色度函数和提色块方法相结合的车窗定位算法，并在车型识别系统中利用，取得了一定的实验效果。     

固体火箭发动机内流场解析解与数值解

对具有燃面减退的固体火箭发动机燃烧室头部的内流场进行了研究。以二维轴对称流动的不可压N-S方程为基础,介绍了解析方法,包括运用空间相似与时间相似原理简化N-S方程以及运用摄动法求解高阶微分方程。之后采用FLUENT软件中的动网格技术,对该问题进行了数值模拟。主要研究了在不同的燃面减退速率及注入雷诺数下,燃烧室头部区域的速度场,压力场及剪应力的分布。通过解析结果与数值结果的比较,发现两者基本吻合,增强了解析解的可信程度。     

Estimation of precipitable water vapour from GPS measurements in Argentina: Validation and qualitative analysis of results

This paper presents PWV estimates from GPS data computed at four continuously operated GPS stations in Argentina established at Buenos Aires, Córdoba, Rosario and Salta over a 1 year period (2006–2007). The objective is to analyze the behaviour of the GPS PWV estimation using mean tropospheric temperature (_Tm$T_m$) values from the Bevis model, Sapucci model and obtained by a numerical integration of variables provided by the operational analysis of the National Centre of Environmental Prediction (NCEP). The results are validated using PWV values from nearest radio soundings. Moreover, a comparison between PWV values determined from microwave sensors deployed on the NOAA-18 satellite and PWV from GPS observations is also presented.