基于卡尔曼滤波的D-InSAR和水准监测数据融合方法研究

针对面状监测的D-InSAR技术在沉降最大值附近精度较低,而高精度水准测量只能得到有限个监测点变形值的缺陷,利用集合卡尔曼滤波方法对D-InSAR和水准监测结果进行同化,从而对目标进行高精度面状沉降变形监测。结果表明,基于集合卡尔曼滤波同化结果相比于反演值和D-InSAR监测结果有了很大改善。将该方法应用于济宁邹济公路,经同化值的总均方根误差为17.7 mm,满足高等级公路变形监测的精度要求。     

集合卡尔曼滤波在流域水文模型流量预报中的应用

针对流域降雨入渗过程,引入集合卡尔曼滤波(EnKF)理论,视整个边坡流域为一个随机动态系统,将边坡流域流量观测值作为系统的输出,用集合卡尔曼滤波模型来描述系统的状态;结合流域流量计算方法,实现水文模型参数的随机动态估计,在有效获得待估参数的同时还给出估计值的不确定性.通过数值算例表明,集合卡尔曼滤波可以有效地对含噪声的量测数据进行处理,能够跟踪水文模型的动态变化.相对于常用最优化算法,集合卡尔曼滤波同时给出反演结果和先验知识的后验分布,显示出更好的实时性和可靠性.     

一种基于GPS/BDS观测值的抗多路径误差导航定位算法

提出一种抑制卫星导航定位中多路径误差的算法,采用双卫星系统GPS/BDS的伪距和多普勒观测值,增加了观测值数据的有效性,结合抗差自适应卡尔曼滤波模型,减弱了城市稠密建筑中卫星导航定位多路径误差对定位的影响。城市车载实验结果显示,与GPS单系统伪距定位相比,采用GPS/BDS伪距定位,点位精度由4.9 m提高到4.3 m;采用本文算法,点位精度进一步提高到3.4 m,验证了本文算法的有效性。     

相邻历元误差相关的抗差卡尔曼滤波算法分析

根据卡尔曼滤波理论以及抗差理论,本文推导出了相邻历元误差相关的抗差卡尔曼滤波模型,其对观测值中含有粗差有良好的抗差性。通过对含有粗差的变形监测数据分析,与相邻历元误差相关的卡尔曼滤波模型进行比较,采用本文构造的抗差卡尔曼滤波模型处理数据,无论是否有粗差存在观测值里,变形计算结果与实际结果大体一致,粗差对计算结果的影响不敏感。在对变形监测数据分析时,可得出卡尔曼滤波方法估计的状态向量,没有寄存大量的以往观测数据,而是使用最近的观测数据,经过不断的预测和改正,把新的状态展示在系统中。     

通用劈窗算法的NOAA-18(N)AVHRR/3数据地表温度遥感反演与验证

本文以NOAA-18(N)AVHRR/3数据,运用通用劈窗技术获得地表温度。首先,利用MODTRAN 4模拟不同地表和大气状况下热红外通道(Ch4,10.3~11.3μm和Ch5,11.5~12.5μm)的星上亮温,并建立模拟数据库。其次,按照地表温度、大气可降水汽含量、地表比辐射率和观测天顶角,对模拟数据库分组,确定出各分组的通用劈窗算法系数。然后,将构建的地表温度反演模型应用到NOAA-18(N)AVHRR/3数据,模型所需的地表比辐射率由NDVI阈值法确定,大气可降水汽含量是利用Li等(2003)提出的一种劈窗的协方差与方差比的方法来估算。反演结果表明:在观测天顶角小于30°或者大气可降水汽含量小于3.5 g/cm2时,地表温度反演的均方根误差小于1.0K;在观测天顶角小于45°并且大气可降水汽含量小于5.5g/cm2情况下,均方根误差小于1.5K。最后,利用美国通量站的实测数据对地表温度反演结果进行了验证,结果表明均方根误差小于1.8K。     

区域CORS网观测数据实时对流层延迟建模

针对高精度实时定位中区域天顶对流层延迟ZTD的时空特性导致插值精度不高的问题,提出一种基于高程归算的克里金插值模型。采用区域CORS网观测数据解算ZTD用于建模分析,相比于现有的其他方法,该模型能够提升插值精度,参与建模测站数量较少时其均方根误差RMSE仍可保持在10 mm以内,为区域实时高精度定位提供了基础。     

基于高斯过程回归和高压压汞测定致密砂岩渗透率:以鄂尔多斯盆地长7段致密砂岩为例

致密砂岩由于滑脱效应的存在, 其气测渗透率存在一定误差, 测定绝对渗透率对明确致密砂岩渗流特征有重要意义。高斯过程回归方法是目前最先进的机器学习算法, 在处理石油领域非线性和多维数复杂问题具有优势。以鄂尔多斯盆地姬塬地区长7段致密砂岩为研究对象, 将平方指数(SE)和马特恩(Matern)函数作为高斯过程回归模型中两个协方差函数, 通过高压压汞测试的孔隙度、未饱和汞体积比、门槛压力和分形维数来预测致密砂岩的绝对渗透率, 并结合误差分析来研究不同协方差模型预测渗透率的效果。结果表明, 马特恩协方差(Matern)模型的相对误差均值(MMRE)、均方根误差(RMSE)、标准偏差(STD)分别为32%, 0.16和0.57, 准确度较高, 尤其当渗透率小于0.1×10-3 μm2时, 马特恩协方差(Matern)模型精度明显好于平方指数协方差(SE)模型和Winland经验公式。致密砂岩用马特恩模型预测渗透率精度更高。此外, 敏感性分析表明孔隙度对渗透率正影响最大, 门槛压力对渗透率负影响最大; 杠杆值和标准化残差证明高斯过程回归模型预测渗透率的有效性。综上, 马特恩协方差(Matern)模型对渗透率小于0.1×10-3 μm2致密砂岩适用性好, 对微纳米级孔喉发育的致密砂岩勘探评价有重要意义。      

基于CatBoost的植被总初级生产力遥感模拟方法及在福建省的应用

植被总初级生产力(GPP)作为衡量陆地生态系统健康的重要指标,可直接反映区域环境状况和改善情况,因此准确估算植被GPP变化对区域可持续发展具有重要意义。本文利用中国及日本涡度通量观测数据,构建了基于CatBoost算法融合地形特征的GPP估算模型;并将模型应用于具有复杂地形特征的福建省,实现了该省GPP长时序模拟。研究结果表明：(1)地形特征是GPP机器学习估算的重要参数,融合地形特征建模的GPP模拟结果精度明显提高,均方根误差(RMSE)下降16%。(2) CatBoost GPP估算模型有效降低了传统GPP估算模型和常用机器学习(随机森林和支持向量机)GPP估算模型中存在的高估和低估现象,模型拥有更高的精度和更强的鲁棒性。本文GPP模拟精度：决定系数(R²)为0.888,RMSE为1.164 gC·m^-2·day^-1,平均绝对误差(MAE)为0.773 gC·m^-2·day^-1。(3)基于CatBoost GPP估算模型模拟的福建省多年GPP变化与GOSIF GPP估算结果...     

CNS/SINS组合导航系统的UKF算法研究

天文/捷联惯性（CNS/SINS）组合导航系统采用姿态组合,可使姿态角处于收敛状态,并有效抑制位置及速度的发散。为提高组合导航系统的精度,本文设计了CNS/SINS组合导航系统的UKF算法,在建立CNS/SINS组合导航系统非线性状态方程及线性量测方程的基础上,首先对UKF的量测更新过程进行简化,降低其计算量;然后,基于平台误差角提出系统状态协方差矩阵中姿态角协方差矩阵的计算方法,并推导了UKF算法中姿态量测值一步预测误差对应的平台角误差向量表达式,进而建立CNS/SINS组合导航系统的UKF算法;最后进行仿真实验。结果表明,相对于线性卡尔曼滤波算法及EKF算法,本文算法可明显提高组合导航系统的各导航参数精度,并且本文算法对滤波器初始姿态角误差变化具有较高的鲁棒性。     

利用无几何模型求解GPS三频模糊度的成功率分析

利用无几何模型求解GPS模糊度实数解是不以基线分量为未知数的线性模型,码观测量几乎直接用于确定模糊度,即使在较短的观测时间内,也不会出现设计矩阵复共线性,对模糊度求解具有明显优势。利用Kronecker乘积导出了利用无几何模型求解三频模糊度及其协方差矩阵的表达式,分析得到该协方差矩阵只与伪距噪声和相位噪声之间的结构以及采用的历元数相关,与接收机和卫星之间的几何构形无关;整数变换Z矩阵只与伪距噪声和相位噪声之间的结构相关的有益结论。最后利用无几何模型分别计算了单频、双频、三频模糊度求解的成功率, 得出对于双频和三频只需少数历元即可成功固定模糊度,特别对于三频观测,甚至单历元即可成功固定模糊度。     

局地化方法在集合转换卡尔曼滤波同化的适用性研究

为了探索协方差局地化（Covariance Localization,CL）方法在集合转换卡尔曼滤波（Ensemble Transform Kalman Filter,     

A homogeneous linear estimation method for system error in data assimilation

In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in previous literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pressure (SLP). A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condition. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduction of analysis errors. The background error covariance structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ‘correlation scale’ is introduced. However, the new method needs further evaluation with more cases of assimilation.     

Assimilation of high frequency radar data into a shelf sea circulation model

High Frequency (HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation (OI) method. The purpose of this work is to develop a real-time computationally highly efficient assimilation method to improve the forecast of shelf current. Since the true state of the ocean is not known, the specification of background error covariance is arduous. Usually, it is assumed or calculated from an ensemble of model states and is kept in constant. In our method, the spatial covariances of model forecast errors are derived from differences between the adjacent model forecast fields, which serve as the forecast tendencies. The assumption behind this is that forecast errors can resemble forecast tendencies, since variances are large when fields change quickly and small when fields change slowly. The implementation of HF radar data assimilation is found to yield good information for analyses. After assimilation, the root-mean-square error of model decreases significantly. Besides, three assimilation runs with variational observation density are implemented. The comparison of them indicates that the pattern described by observations is much more important than the amount of observations. It is more useful to expand the scope of observations than to increase the spatial interval. From our tests, the spatial interval of observation can be 5 times bigger than that of model grid.     

Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling.However,there is much uncertainty in the assimilation process,which affects the assimilation results.This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter(EnKF)and Genetic Algorithm(GA).A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model(DHSVM)was coupled with a semi-empirical backscattering model(Oh).The Advanced Synthetic Apertture Radar(ASAR)data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment.In order to improve the assimilation results,a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR.The EnKF and GA were used to re-initialize and re-parameterize the simulation process,respectively.The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data.The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.     

基于效用值的模糊最短路问题的研究

模糊数学是研究和处理模糊现象的一种数学方法,而最短路径问题一直是运筹学、地理信息科学、计算机科学等学科的一个研究热点,被广泛地应用于交通运输、通讯工程、计算机网络和供应链管理等领域．模糊最短路问题的求解,实质就是比较模糊数的序关系,对模糊数进行排序,从而得出模糊最短路问题的结果．在基于对效用值的研究基础上,综合考虑了模糊数隶属函数的分布情况,得到一种新的三角模糊数和梯形模糊数的排序．并应用于求解模糊最短路问题,获得了求解模糊最短路问题的新算法．通过几个实例,验证了方法的有效性和实用性．     

附有模糊先验信息的病态问题解算方法

以模糊理论为基础，用模糊数集来描述未知参数向量中的模糊先验信息，将其以约束条件的形式纳入数学模型。给出带有模糊先验信息的平差模型，并基于正态模糊数建立新的平差准则，提出一种带有模糊先验信息的平差新算法。实例验证，新方法可以较好地解决测量平差中法方程病态问题。未知参数的误差分析显示，新方法解算病态方程优于最小二乘估计、截断奇异值法以及岭估计。     

Assimilating ASAR data for estimating soil moisture profile using an ensemble Kalman filter

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar （ASAR） observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter （EnKF）, the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model （DHSVM） was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were con- ducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.     

基于局部空间信息KFCM的遥感图像聚类算法

针对模糊C均值(Fuzzy C-Means, FCM)算法,不能有效地对夹杂噪声的遥感图像聚类的问题,本文提出了一种基于局部空间信息核模糊C均值(Kernel Fuzzy C-Means, KFCM)的遥感图像聚类算法。首先,运用核函数将遥感图像的所有像元映射到高维特征空间,通过非线性映射优化遥感图像的有用特征;然后,根据相邻像元之间的相关性,利用一种空间函数重新定义像元的模糊隶属度,将像元的局部空间信息引入到FCM算法中,并在高维特征空间中使用这种基于局部空间信息的FCM算法对像元聚类。由于引入了像元的局部空间信息,算法可以直接应用于原始遥感图像,不需要滤波预处理。大量实验结果表明,本文提出的基于局部空间信息KFCM的遥感图像聚类算法具有较强的抗噪能力,可得到较好的同质区域,优于现有的FCM算法、模糊局部信息C均值(Fuzzy Local Information C-Means, FLICM)算法及KFCM算法。     

基于梯形模糊数GIS产品的可靠性分析

分析现有GIS产品的可靠性方法没有考虑GIS产品的模糊性，首次提出了一种基于梯形模糊数算术运算的可靠性分析方法，论述了GIS产品模糊可靠性分析的意义，简单介绍了梯形模糊数的基本概念和运算规则，阐述了梯形模糊数用于GIS产品可靠性分析的步骤，并用实例进行分析，最后总结并指出了需要解决的问题。结果表明，该方法可以较好地表达GIS产品的不确定性，而且计算也十分简单。