Václav Smídl

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– The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily accuracy. However, the curse of dimensionality prevents its application in cases where the state dimension-ality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach(More)
Knowledge of the noise distribution is typically crucial for the state estimation of general state-space models. However, properties of the noise process are often unknown in the majority of practical applications. The distribution of the noise may also be non-stationary or state dependent and that prevents the use of off-line tuning methods. For linear(More)
Factor Analysis (FA) is a well established method for factors separation in analysis of dynamic medical imaging. However , its assumptions are valid only in limited regions of interest (ROI) in the images which must be selected manually or using heuristics. The resulting quality of separation is sensitive to the choice of these ROI. We propose a new(More)
— This paper presents a novel controller for nonlin-ear unconstrained systems, coined as Extended Rauch-Tung-Striebel (ERTS) controller. The controller is derived from a general framework based on the duality between optimal control and estimation established by Todorov. The proposed controller uses Rauch-Tung-Striebel smoother that predicts (filters)(More)
Blind source separation algorithms are based on various separation criteria. Differences in convolution kernels of the sources are common assumptions in audio and image processing. Since it is still an ill posed problem, any additional information is beneficial. In this contribution , we investigate the use of sparsity criteria for both the source signal(More)
In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of(More)