用GPS秒信号锁定高频振荡器的方法研究

对用远距离传输的GPS秒信号锁定本地高频晶体振荡器的方法进行了探讨。介绍了高精度的时间数字转换器TDC(Time to Digital Converter)和对滤波的方法进行了探讨。为了降低成本又能够满足设计要求的精度，还考虑了双D／A转换的方法。有关的硬件系统已经通过了调试和一系列的测试和实验，在测量和控制精度方面，能达到设计的要求。此外，提出了这个系统有待解决的问题，如双D／A转换的非线性问题，Kalman滤波抗野值及继续提高精度的问题。     

南极冰貌卫星影像条带噪声的剔除

本文在简要叙述条带噪声形成原因的基础上 ,针对南极地区的ASTER 1A的可见光影像中出现的条带噪声的现象 ,通过设计了基于切比雪夫窗、凯瑟窗的FIR滤波器和约束最小二乘FIR滤波器对其条带噪声进行剔除 ,这两种滤波器能够尽可能地剔除条带 ,同时使影像的灰度分布发生最小畸变。并通过与其他方法 (例如 :低通滤波 ,直方图匹配等 )比较 ,说明这两种滤波的方法优于其他的方法     

Data Fusion in Distributed Multi-sensor System

This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors“ data processing. First, a residual X^2-test strategy with the corresponding algorithm is designed.Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real-time data. A pseudolite (PL) simulation example is given.     

基于光谱信息的遥感图像空间域自适应滤波

遥感图像往往由于内外因素的干扰含有各种噪声 ,为提高图像质量人们对其进行滤波。常规的滤波方式完全基于图像空间域 ,在消除异常点使图像平滑的同时使边界变得不清楚从而使图像模糊 ;或者正相反 ,在边缘信息得到增强时也放大了图像噪声而使图像变得粗糙。该文的自适应滤波方法充分利用多光谱和高光谱遥感图像的光谱维信息 ,滤波时不仅要使均匀的地块得到平滑 ,而且要使地块间的边缘、局部的异常点、线仍然得到保存、在光滑的同时尽量保持原有的数值特征     

Simultaneous identification of time and space variant dynamic soil properties during the 1995 Hyogoken-Nanbu earthquake

Time and space variant soil properties at a liquefied site were simultaneously identified in the time domain by using borehole array strong motion records. During soil liquefaction at a site, soils usually show a wide variety of non-linear behavior along the depth as well as non-stationary behavior. Strong ground motion records were obtained at Port Island borehole array observatory, Kobe, during the 1995 Hyogoken-Nanbu earthquake. In this study, the instrumented soil was modeled by the equivalent linear MDOF system, and an extended Kalman filter with local iteration was employed for the identification of the soils. The identification process was successfully conducted, and the stress–strain relationships of the soils at the liquefied site were obtained from different depths all at once.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

Combining two filter paper‐based analytical methods to monitor temporal variations in the geochemical properties of fluvial suspended particulate matter

Many of the commonly used analytical techniques for assessing the properties of fluvial suspended particulate matter (SPM) are neither cost effective nor time efficient, making them prohibitive to long‐term high‐resolution monitoring. We present an in‐depth methodology utilizing two types of spectroscopy which, when combined with automatic water samplers, can generate accurate, high‐temporal resolution SPM geochemistry data, inexpensively and semi‐destructively, directly from sediment covered filter papers. A combined X‐ray fluorescence spectroscopy and diffuse reflectance infrared Fourier transform spectroscopy approach is developed to estimate concentrations for a range of elements (Al, Ca, Ce, Fe, K, Mg, Mn, Na, P, Si, Ti) and compounds (organic carbon, Al_dithionate, Al_oxalate, Fe_dithionate, and Fe_oxalate) within SPM trapped on quartz fibre filters at masses as low as 3 mg. Calibration models with small prediction errors are derived, along with mass correction factor models to account for variations in retained SPM mass. Spectral pre‐processing methods are shown to enhance the reproducibility of results for some compounds, and the importance of filter paper selection and homogeneous sample preparation in minimizing spectral interference is emphasized. The geochemical signal from sediment covered filter papers is demonstrated to be time stable enabling samples to be stored for several weeks prior to analysis. Example results obtained during a heavy precipitation event in October 2012 demonstrate the methodology presented here has considerable potential to be utilized for high‐resolution monitoring of SPM geochemistry under a range of in‐stream hydrological conditions. Copyright © 2013 John Wiley & Sons, Ltd.     

基于NDVI时间序列数据的江西省水稻种植制度变化研究

利用Savitzky-Golay滤波对覆盖江西省范围的SPOT VGT NDVI时间序列数据进行平滑处理的基础上,结合坡度数据,通过非监督分类的方法提取了江西省2000、2005和2010年水稻种植范围,并根据NDVI的年内动态变化,从水稻种植范围、水稻生长季起始时间、水稻复种指数和NDVI最大振幅等分析了江西省水稻种植和生长情况,探讨2000~2010年江西省水稻生产的变化。     

基于方差递推法的Kalman滤波在钟差预报中的应用

卫星导航系统中星载原子钟的钟差预报对于导航、定位及授时具有重要的作用。为了提高卫星钟差预报的精度,设计了一种两步确定卫星钟噪声协方差矩阵的Kalman滤波钟差预报模型。该方法首先基于Hadamard总方差确定卫星钟噪声协方差矩阵的初值,然后,使用方差递推法得到滤波过程中卫星钟的噪声协方差矩阵。使用GPS系统的星载铷钟数据进行短期预报,并与常用的二次多项式模型、灰色模型进行对比,结果表明:本文中提出的方法可以实现高精度的卫星钟差预报且预报效果优于两种常用模型,同时,该方法能够在一定程度上弥补预报误差随预报时间增加而不断变大的不足。     

PPP/SINS紧耦合系统仿真的研究与分析

在捷联惯导(SINS)和GPS卫星接收机进行紧耦合的研究中,采用差分定位进行紧耦合的方法比较成熟,而关于精密单点定位(PPP)与捷联惯导紧耦合的研究还比较少。本文对精密单点定位与捷联惯导紧组合系统进行了Matlab仿真,利用数学解析的方法产生机载运动轨迹,通过设置系统的参数,获得仿真SINS和GPS数据;然后,通过PPP/SINS紧组合系统的仿真程序解算,将定位结果与PPP的结果比较,表明PPP/SINS紧组合导航定位的结果比PPP的精度和可靠性好,而且收敛的速度更快,同时也验证了算法的正确性。最后,分析了不同等级惯导对定位精度的影响。