基于DEnKF的背景误差协方差局地化和协方差膨胀研究

尽管DEnKF同化不会引入观测采样误差,但小集合仍会造成背景误差协方差矩阵存在伪相关,出现滤波发散。为了减少小集合对数据同化结果的影响,结合Lorenz96模型和DEnKF同化方案分析了协方差局地化和协方差膨胀方法对背景误差协方差矩阵、增益矩阵及同化结果的影响。实验表明:协方差局地化方法能消除背景误差协方差矩阵和增益矩阵中的伪相关,增大背景误差协方差矩阵的秩,有助于滤波算法收敛到真实解;而协方差膨胀方法不能消除背景误差协方差矩阵和增益矩阵中的伪相关,只能改善在每个同化周期内背景误差协方差系统性被低估的现象;同化过程中采用合适的局地化半径和方差膨胀因子能够较好地改善同化结果的精度。     

基于Lorenz-96模型的顺序数据同化方法比较研究

顺序数据同化方法在数据同化系统中得到了广泛的应用,其性能各有优缺。选择3种典型的顺序数据同化算法,即集合Kalman滤波,集合转换Kalman滤波和确定性Kalman滤波,使用经典的Lorenz-96模型进行敏感性实验,研究不同的关键参数变化,如集合数目变化、观测数变化、误差放大因子变化和定位半径变化时对同化效果的影响。实验表明：集合数目和观测数目的多少直接影响3种方法的同化效果;协方差放大因子和定位半径的选择会提高同化精度。综合比较,确定性集合Kalman滤波算法是一种具有较强鲁棒性的滤波算法,能够在集合数较小的情况下达到较好的同化效果。     

耦合模糊控制算法的数据同化观测误差处理方法

针对数据同化过程中集合数目有限情形下的虚假相关问题,通过模糊控制算法判断观测点与状态更新点之间的距离,构造观测位置等价权重,与集合转换卡尔曼滤波方法相结合,提出一种新的数据同化方法。利用经典的Lorenz-96混沌模型,比较分析集合转换卡尔曼滤波(ETKF),局地化集合转换卡尔曼滤波(LETKF)和模糊控制数据同化算法(FETKF)在不同参数变化时的性能,由此探讨3种方法的优劣。研究结果表明:新方法能够使每一步状态更新获得更有效的观测信息,减小因观测数据难以得到有效利用而带来的误差,同时避免了同化过程中的虚假相关问题,从而提高滤波精度。     

迭代集合卡尔曼滤波方法的性能比较研究

针对数据同化过程中模型的非线性问题,通过分析对比得出了一种适合强非线性系统的迭代集合Kalman滤波(IEnKF)。在Lorenz\|63模型的框架内,比较分析集合Kalman滤波(EnKF)、迭代集合Kalman滤波(IEnKF)和迭代扩展卡Kalman滤波(IEKF)在集合数、观测误差方差、放大因子和模型步长不同时同化性能差异,由此探讨这3种方法的优劣。研究结果表明:随着集合数的增加,3种算法的同化性能都得到了一定的改善;放大因子的增大,使其同化性能变差且EnKF呈现出多重波峰波谷的现象;3种方法的均方误差(RMSE）随观测误差方差和模型步长的增大而增大,其同化精度都变差;而IEnKF同化性能最优,更具有鲁棒性。     

耦合多目标遗传算法的数据同化方法参数优化研究

针对影响数据同化系统性能的数据同化方法中多参数优化问题,设计了一种耦合多目标优化非支配排序遗传算法(Non-dominated Sorting Genetic Algorithms,NSGA-2)的优化框架。以Lorenz-96模型为研究对象,局地化集合转换卡尔曼滤波(Local Ensemble Transform Kalman Filter,LETKF)为实验算法,对影响其同化性能的分析膨胀因子和协方差膨胀因子进行联合优化实验。同时针对计算时间过长问题,对该框架进行并行改进。研究结果表明:该框架针对变量范围较大的参数空间,具有良好的多变量寻优效果,鲁棒性较强,并行设计节省运行时间,耦合多目标遗传算法的数据同化方法整体寻优框架设计实现简单,进一步将验证其在陆面数据同化中的应用前景。     

Kalman滤波在井下人员跟踪定位中的应用

针对基于RSSI定位算法在定位过程中不具备连续性问题,提出了一种基于Kalman滤波的连续性井下人员定位方法。采用Kalman滤波对基于RSSI定位算法估算出的井下人员位置坐标进行滤波处理,在此坐标的基础上,建立Kalman滤波模型,利用Kalman滤波实现对井下人员的实时跟踪。实验结果表明,基于Kalman滤波的定位方法对井下人员的跟踪效果较好,提高了系统的实时性和跟踪精度。     

主动信号检测中干扰背景的高斯化处理

非高斯干扰下的主动信号检测中,高斯化模块往往不可或缺。通过对样本的抑大扬小,它可以有效地增强背景数据的高斯性,提高后续预白化和相关检测的性能。本文给出高斯化滤波的定义和效果评价方法,然后对比研究了基于概率密度函数及导数的U滤波和基于累积分布函数及反函数的G滤波两种高斯化实现方法、原理与性能,并使用一组湖试数据测试了它们各自的高斯化效果。最后,举例说明了高斯化在混合高斯Rao检测中的应用。     

多传感器滤波融合的惯性定位算法

针对在导航系统姿态解算中,陀螺仪和电子罗盘在解算姿态时分别存在积分误差和磁场干扰的问题,提出了利用Kalman滤波和互补滤波相融合的算法进行定位。首先将电子罗盘和陀螺仪通过Kalman滤波得出最优估计四元数,然后利用互补滤波算法对陀螺仪的漂移进行补偿得到校正后的四元数,将此次得到的四元数和Kalman滤波得出最优估计四元数再次通过Kalman滤波对四元数进行第二次最优估计,进而输出姿态角。实验中对比了本算法和互补滤波算法、无滤波算法的效果。实验证明,该算法不仅可以有效解决方位角误差发散问题,还有效解决了磁场干扰问题,实现了高精度的方位输出。     

基于Lorenz-63模型的状态与参数同时估计方法对比研究

模型状态同化精度受多种方面因素的影响,针对状态同化中模型参数的不确定性问题,状态与参数同时估计为此提供了一种较好的解决方案,即在进行状态同化的过程中得到合理的参数估计值。在Lorenz-63模型的基础上构建状态与参数同时估计框架,比较分析增广集合卡尔曼滤波(AEnKF,Augmented Ensemble Kalman Filter)、双重集合卡尔曼滤波(DEnKF,Dual Ensemble Kalman Filter)和同时优化与同化方法(SODA,Simultaneous Optimization and Data Assimilation)在集合数、观测误差和观测数不同时的参数和状态估计结果差异,由此探讨3种方法的优劣及适用性。研究结果表明:3种方法都能较好地估计模型的状态和参数,AEnKF的误差在集合数不大于20时最大,随着集合数增加降低的速率最小;3种方法的RMSE值随观测误差的增大而增大,但算法间差异不大;观测数变为1时3种方法的结果都变差,其中AEnKF最明显。     

卡尔曼波形估计法对比研究与雷达跟踪应用

目标跟踪问题,主要应用于雷达数据的处理过程中。目前在雷达跟踪方面有很多滤波方式可以研究和使用,典型算法有最小二乘法滤波、维纳滤波、卡尔曼滤波和自适应滤波等。本文将维纳滤波和卡尔曼滤波比较,通过实验来研究卡尔曼滤波的特性。最后本文简要讨论在蒙特卡罗方法下采用Kalman滤波方法对单个目标点迹进行预测,并借助于Matlab仿真工具,对实验的效果进行评估。     

陆面数据同化系统及其在全球变化研究中的应用

陆面数据同化的研究起源于20世纪末,发展比较迅速。本文从当前发展比较成熟的陆面数据同化系统、同化方法以及陆面数据同化在全球变化研究中的应用三方面做了综述。比较成熟的陆面数据同化系统主要是美国(全球)和欧洲的陆面数据同化系统,加拿大、韩国和我国的数据同化系统最近才有所发展;数据同化方法主要以基于统计估计理论和以基于变分的两大类方法为主流,近年来人工神经网络和粒子滤波技术有所发展;在生态水文学、碳循环和作物估产的应用研究中,陆面数据同化方法已经发挥了重要作用。     

Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter

Ensemble Kalman filter is a new sequential data assimilation algorithm which was originally developed for atmospheric and oceanographic data assimilation. It can be applied to calculate error covariance matrix through Monte-Carlo simulation. This approach is able to resolve the nonlinearity and discontinuity existed within model operator and observation operator. When observation data are assimilated at each time step, error covariances are estimated from the phase-space distribution of an ensemble of model states. The error statistics is then used to calculate Kalman gain matrix and analysis increments. In this study, we develop a one-dimensional soil moisture data assimilation system based on ensemble Kalman filter, the Simple Biosphere Model (SiB2) and microwave radiation transfer model (AIEM, advanced integration equation model). We conduct numerical experiments to assimilate in situ soil surface moisture measurements and low-frequency passive microwave remote sensing data into a land surface model, respectively. The results indicate that data assimilation can significantly improve the soil surface moisture estimation. The improvement in root zone is related to the model bias errors at surface layer and root zone. The soil moisture does not vary significantly in deep layer. Additionally, the ensemble Kalman filter is predominant in dealing with the nonlinearity of model operator and observation operator. It is practical and effective for assimilating observations in situ and remotely sensed data into land surface models.     

A problem-solving environment for data assimilation in air quality modelling

A generic toolbox for data assimilation called COSTA (COmmon Set of Tools for the Assimilation of data) makes it possible to simplify the application of data assimilation to models and to try out various methods for a particular model. Concepts of object oriented programming are used to define building blocks for data assimilation systems that can be exchanged and reused. The main building blocks in COSTA are the model component and the stochastic observer component. These components can be created by wrapping existing code. The LOTOS–EUROS air quality model will be used for operational smog and aerosol forecasts in the Netherlands in the near future. The COSTA framework will be used in this operational environment to implement the data assimilation techniques. As a first steps towards this operational system the model component of LOTOS–EUROS is created and the performance of various Kalman filter based data assimilation techniques are compared for a real live case study. The ensemble Kalman filter and the ensemble square-root filters converged faster than the other tested techniques for the selected case study setup.     

Improved Tabu Search and Simulated Annealing methods for nonlinear data assimilation

Nonlinear data assimilation can be a very challenging task. Four local search methods are proposed for nonlinear data assimilation in this paper. The methods work as follows: At each iteration, the observation operator is linearized around the current solution, and a gradient approximation of the three dimensional variational (3D-Var) cost function is obtained. Then, samples along potential steepest descent directions of the 3D-Var cost function are generated, and the acceptance/rejection criteria for such samples are similar to those proposed by the Tabu Search and the Simulated Annealing framework. In addition, such samples can be drawn within certain sub-spaces so as to reduce the computational effort of computing search directions. Once a posterior mode is estimated, matrix-free ensemble Kalman filter approaches can be implemented to estimate posterior members. Furthermore, the convergence of the proposed methods is theoretically proven based on the necessary assumptions and conditions. Numerical experiments have been performed by using the Lorenz-96 model. The numerical results show that the cost function values on average can be reduced by several orders of magnitudes by using the proposed methods. Even more, the proposed methods can converge faster to posterior modes when sub-space approximations are employed to reduce the computational efforts among iterations.     

An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation

This paper aims to investigate several new nonlinear/non-Gaussian filters in the context of the sequential data assimilation. The unscented Kalman filter (UKF), the ensemble Kalman filter (EnKF), the sampling importance resampling particle filter (SIR-PF) and the unscented particle filter (UPF) are described in the state-space model framework in the Bayesian filtering background. We first evaluated those methods with a simple highly nonlinear Lorenz model and a scalar nonlinear non-Gaussian model to investigate the filter stability and the error sensitivity, and then their abilities in the one-dimensional estimation of the soil moisture content with the synthetic microwave brightness temperature assimilation experiment in the land surface model VIC-3L. All the results are compared with the EnKF. The advantages and disadvantages of each filter are discussed.The results in the Lorenz model showed that the particle filters are suitable for the large measurement interval assimilation and that the Kalman filters were suitable for the frequent measurement assimilation as well as small measurement uncertainties. The EnKF also showed its feasibility for the non-Gaussian noise. The performance of the SIR-PF was actually not as good as that of the UKF or the EnKF regarding a very small observation noise level compared with the uncertainties in the system. In the one-dimensional brightness temperature assimilation experiment, the UKF, the EnKF and the SIR-PF all proved to be flexible and reliable nonlinear filter algorithms for the low dimensional sequential land data assimilation application. For the high dimensional land surface system that takes the horizontal error correlations into account, the UKF is restricted by its computational demand in the covariance propagation; we must use the EnKF, the SIR-PF and other covariance reduction algorithms. The large computational cost prevents the UPF from being applied in practice.     

基于Stacking-Bagging-Vote多源信息融合模型的财务预警应用

集成重采样技术可以在一定程度上解决财务预警研究中样本的不平衡性难题,而不同的集成模型与不同的重采样集成技术有不同的适配性.研究发现,Up-Down集成采样与Tomek-Smote集成采样分别适配于Bagging-Vote集成模型和Stacking融合模型.基于此,构建了Stacking-Bagging-Vote(SBV...     

RFID阅读器相位噪声的距离相关效应研究

相位噪声是设计射频系统时的一个重要参数,它对系统性能有很大影响。对于射频识别(RFID)系统,距离相关效应对其相位噪声有重要影响。研究了无源超高频(UHF)RFID阅读器相位调制(PSK)信号的相位噪声表达式,导出距离相关效应时的相位噪声功率谱密度(PSD)和通信误符号率(SER)计算式。距离相关效应下相位噪声的测量和仿真表明,解析结果与测量、仿真结果一致。