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Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter 总被引:8,自引:0,他引:8
Brian R. Hunt Eric J. Kostelich Istvan Szunyogh 《Physica D: Nonlinear Phenomena》2007,230(1-2):112-126
Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system’s time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to “forecast” the current state, using a prior state estimate (which incorporates information from past data) as the initial condition, then uses current data to correct the prior forecast to a current state estimate. This Bayesian approach is most effective when the uncertainty in both the observations and in the state estimate, as it evolves over time, are accurately quantified. In this article, we describe a practical method for data assimilation in large, spatiotemporally chaotic systems. The method is a type of “ensemble Kalman filter”, in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states. We discuss both the mathematical basis of this approach and its implementation; our primary emphasis is on ease of use and computational speed rather than improving accuracy over previously published approaches to ensemble Kalman filtering. We include some numerical results demonstrating the efficiency and accuracy of our implementation for assimilating real atmospheric data with the global forecast model used by the US National Weather Service. 相似文献
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Time-dependent generalized polynomial chaos 总被引:1,自引:0,他引:1
Marc Gerritsma Jan-Bart van der Steen Peter Vos George Karniadakis 《Journal of computational physics》2010,229(22):8333-8363
Generalized polynomial chaos (gPC) has non-uniform convergence and tends to break down for long-time integration. The reason is that the probability density distribution (PDF) of the solution evolves as a function of time. The set of orthogonal polynomials associated with the initial distribution will therefore not be optimal at later times, thus causing the reduced efficiency of the method for long-time integration. Adaptation of the set of orthogonal polynomials with respect to the changing PDF removes the error with respect to long-time integration. In this method new stochastic variables and orthogonal polynomials are constructed as time progresses. In the new stochastic variable the solution can be represented exactly by linear functions. This allows the method to use only low order polynomial approximations with high accuracy. The method is illustrated with a simple decay model for which an analytic solution is available and subsequently applied to the three mode Kraichnan–Orszag problem with favorable results. 相似文献
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We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling’s interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling’s interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling’s interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. 相似文献
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This is a reply to the comment of Dr. Sakov on the work “Ensemble Kalman filter with the unscented transform” of Luo and Moroz (2009) [2]. 相似文献
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The results of numerical experiments with the ensemble unscented Kalman filter and 40-dimensional model of Lorentz and Emanuel in Luo and Moroz (2009) [2] are inconclusive. 相似文献
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In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. We introduce a framework, called the scaled unscented transform Gaussian sum filter (SUT-GSF), which combines two ideas: the scaled unscented Kalman filter (SUKF) based on the concept of scaled unscented transform (SUT) (Julier and Uhlmann (2004) [16]), and the Gaussian mixture model (GMM). The SUT is used to approximate the mean and covariance of a Gaussian random variable which is transformed by a nonlinear function, while the GMM is adopted to approximate the probability density function (pdf) of a random variable through a set of Gaussian distributions. With these two tools, a framework can be set up to assimilate nonlinear systems in a recursive way. Within this framework, one can treat a nonlinear stochastic system as a mixture model of a set of sub-systems, each of which takes the form of a nonlinear system driven by a known Gaussian random process. Then, for each sub-system, one applies the SUKF to estimate the mean and covariance of the underlying Gaussian random variable transformed by the nonlinear governing equations of the sub-system. Incorporating the estimations of the sub-systems into the GMM gives an explicit (approximate) form of the pdf, which can be regarded as a “complete” solution to the state estimation problem, as all of the statistical information of interest can be obtained from the explicit form of the pdf (Arulampalam et al. (2002) [7]).In applications, a potential problem of a Gaussian sum filter is that the number of Gaussian distributions may increase very rapidly. To this end, we also propose an auxiliary algorithm to conduct pdf re-approximation so that the number of Gaussian distributions can be reduced. With the auxiliary algorithm, in principle the SUT-GSF can achieve almost the same computational speed as the SUKF if the SUT-GSF is implemented in parallel.As an example, we will use the SUT-GSF to assimilate a 40-dimensional system due to Lorenz and Emanuel (1998) [27]. We will present the details of implementing the SUT-GSF and examine the effects of filter parameters on the performance of the SUT-GSF. 相似文献
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在电话会议、智能音箱等应用场景下,传声器往往处在声源的远场。混响信号的存在会掩蔽后续到达的直达声信号,降低传声器接收信号的语音质量,以及语音识别系统的准确识别率。多通道线性预测算法是一种经典的盲去混响算法,但该算法往往具有较高的计算复杂度。本文提出了一种简化的卡尔曼滤波更新算法,通过对角化卡尔曼滤波器状态向量误差协方差矩阵,降低了自适应多通道线性预测去混响算法的复杂度。通过与现有分块对角简化算法对比发现,本文提出的简化算法在保证语音质量的同时,进一步降低了原卡尔曼滤波算法的复杂度。 相似文献
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多特征向量约束的自适应环境宽容匹配场算法可用于随机和不确知海洋声传播介质中的声源位置定位。该方法需要对拷贝场复声压的互谱矩阵进行估计。为了克服蒙特卡洛统计方法计算互谱矩阵较为耗时的缺点,文章将起伏介质中的随机或不确知声压表示为以正交的随机多项式为基底的级数,随机多项式基底的输入变量为描述环境随机性或不确知性的随机变量。利用随机多项式展开基底的正交性可快速估计拷贝场复声压互谱矩阵。仿真结果表明:在声源频率较低、浅海海水声速存在随机起伏的条件下,在计算效率上使用随机多项式展开方法估计拷贝场复声压互谱矩阵较蒙特卡洛统计方法可提高一个数量级;在高信噪比下,多特征向量约束匹配场声源定位算法在定位准确率和输出峰均比上优于线性匹配场和对角加载的最小方差匹配场声源定位方法。 相似文献
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嵌入随机多项式的抛物方程不确定声场快速算法 总被引:1,自引:0,他引:1
为了得到准确且高效计算起伏海洋介质中随机声场的算法,本文将随机多项式展开嵌入到宽角抛物方程声场计算模型(简称RAM模型)中,发展了一种不确定声场的快速算法。其计算结果比使用嵌入随机多项式的窄角抛物方程准确,计算时间小于作为参考的蒙特卡洛方法。在仿真算例中,随机多项式展开法对声强均值、方差、概率密度的计算准确;在一定的随机变量维度和随机多项式展开截断幂次内,其计算效率比蒙特卡洛方法至少提高一个数量级。 相似文献
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基于MODIS LAI产品数据集(MOD15A2)构建经验性的LAI动态模型,以LAI作为连接参数,将LAI动态模型与植被辐射传输模型MCRM2相耦合,提出了将耦合模型与时间序列MODIS反射率观测数据集(MOD09A1)同化进行LAI反演的方案.将集合卡尔曼平滑(EnKS)方法引入到LAI同化反演中,为更好地评价该算... 相似文献
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In applications digital image correlation based algorithms often present a basis for analysis of movement/deformation of bodies. The sequence of the obtained images is analyzed for this purpose. Especially, in cases when the body׳s movement/deformation between two successive images is significant, the initial guess can have a major influence on the execution speed of the algorithm. In the worst case it can even cause the divergence of the algorithm. This was the inspiration to develop a new and unique approach for an accurate and reliable determination of an initial guess for each image pixel. Kalman filter has been used for this purpose. It uses past measurements of observed variable(s) for calculations. Beside that it also incorporates state space model of the actual system. This is one of the most important advantages provided by Kalman filter. The determined initial guess by the proposed method is actually close to the true one and it enables fast convergence. Even more important property of this approach is the fact that it is not path-dependant because each image pixel, which is defined in ROI, is tracked through the sequence of images based on its own past measurements and general state space model. Consequently, the proposed method can be used to analyze tasks where discontinuities between image pixels are present. The applied method can be used to predict an initial guess where reference and deformed subsets are related by translational and rotational motion. The advantages mentioned above are verified with numerical and real experiments. The experimental validations are performed by NR (Newton–Raphson) approach which is the most widely used. Beside NR method the presented algorithm is applicable for other registration methods as well. It is used as an addition for calculation of initial guesses in a sequence of deformed images. 相似文献
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The Kalman filter is widely applied in fiber optic gyro (FOG) inertial integrated navigation system. To solve the problem of hard acquirement of Kalman filter parameters, a novel algorithm for FOG GPS/SINS integration navigation based on exact modeling is proposed in this paper. The models of inertial sensors using Allan variance analysis are established in proposed algorithm and the precise Kalman filter model is obtained based on the correspondence between Allan variance coefficients and inertial sensors parameters. The simulation and experimental results show that Kalman filter parameters can be obtained for GPS/SINS integrated navigation system precisely and efficiently based on Allan variance modeling method, and the algorithm has reference value for theoretical perfection and engineering applications. 相似文献
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为减小测量异常误差对非线性目标跟踪系统的影响, 提出了一种基于广义M估计的鲁棒容积卡尔曼滤波算法. 首先将非线性测量方程等价变换, 利用约束总体最小二乘准则构建广义M估计极值函数, 在不进行线性化近似的前提下将其引入到容积卡尔曼滤波求解框架中. 然后根据Mahalanobis距离构建异常误差判别量, 利用卡方分布的置信水平确定判决门限, 并建立改进的三段Huber权函数, 使其能够降低小异常误差权值, 剔除大异常误差. 理论分析表明, 该方法具有无需求导、跟踪精度高、实时性好等优点, 且无需已知异常误差的统计特性; 实验结果表明, 所提算法能够有效减小异常误差的影响, 在实际非线性物理系统中具有广阔的应用空间. 相似文献
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Ali Javed Hashmi Ali EftekharAli Adibi Farid Amoozegar 《Optics Communications》2012,285(24):5037-5043
In this paper, the impact of random synchronization errors on the performance of ground-based telescope array receivers for an inter-planetary optical deep-space communication (ODSC) link is investigated. An adaptive method based on Kalman filters is developed for the synchronization and combination of different telescope signals in the array. An end-to-end simulation platform for ODSC link between Earth and planet Mars is implemented that incorporates pulse-position modulation (PPM), direct-detection array receivers, and photon-counting detectors. The effects of atmospheric turbulence and background noise are also modeled. The performance of array receivers is evaluated in terms of probability of symbol error and achievable data rates. The simulation results show that the Kalman filter-based synchronization scheme keeps the synchronization induced power losses to less than 1 dB. The analysis also shows that in the worst-case operational scenario and presence of random synchronization errors, an array consisting of hundred, 1 m telescopes performs almost similar to a single 10 m telescope. Hence, the degradation in the combined signal due to synchronization errors places a minor limitation on the number of telescopes in a telescope array receiver consisting of up to 100 telescope elements. 相似文献