Using Lorenz96 model with 40 variables, classical methods of advanced data assimilation are explained, implemented and examined. The classical methods include full Kalman filter (KF), extended Kalman filter (EKF), full Kalman smoother (KS), its iterative versions, and sawtooth algorithms (Johnston and Kurishnamurthy 2001). A brief explanation of the theoretical background of ensemble Kalman fil...

Standard latent variable analysis in structural state space models decomposes variables into contributions of shocks (shock decomposition), or the observable (data decomposition). We propose to link shock decomposition and data what we call double decomposition. This allows us better gauge influence on by taking account transmission mechanism each type shock. show usefulness analyzing role esti...

We formulate extended Kalman smoothing in an expec­ tation-propagation (EP) framework. The approximation involved (a local linearization) can be looked upon as a 'collapse' of a non­ gaussian belief state onto a Gaussian form. This formulation al­ lows us to come up with better approximations to the belief states, since we can iterate the algorithm until no further refinement of the beliefs is ...

The application of Kalman filtering methods and maximum likelihood parameter estimation to models of commodity prices and futures prices has been considered by several authors. The usual method of finding the maximum likelihood parameter estimates (MLEs) is to numerically maximize the likelihood function. We present, as an alternative to numerical maximization of the likelihood, a filter-based ...

In this paper, we describe a new and computationally efficient adaptive system for the enhancement of autoregressive (AR) signals which are disturbed by additive white or colored noise and impulsive noise. The system is comprised of an adaptive Kalman filter operating as a fixed lag smoother and a subsystem for AR parameter estimation. A superior performance is achieved by implementing a feedba...

In this paper, we present an iterative Kalman smoother (IKS) for robust 3D localization and mapping, using visual and inertial measurements. Contrary to extended Kalman filter (EKF) methods, smoothing increases the convergence rate of critical parameters (e.g., IMU’s velocity and camera’s clock drift), improves the positioning accuracy during challenging conditions (e.g., scarcity of visual fea...

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in s...

Data assimilation seeks a mathematically optimal compromise between outcomes of a numerical model that simulates a physical system and observations of that system. It has been successfully used for twenty years in operational meteorology to perform the best forecast, and is now being used or tested in many geoscience fields. Two main classes of methods have taken the lead. Firstly, 4D-Var is a ...

Methods for estimating dynamical changes in singletrial Event Related Potentials (ERPs) measurements are presented. The ERPs are modeled with a parametric and a Markov state-space model. Kalman filter and smoother algorithms are used for recursive estimation of the states. The methods are demonstrated with simulations and real measurements of the P300 responses.