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- Václav Smídl, Anthony Quinn
- IEEE Transactions on Signal Processing
- 2008

The use of the variational Bayes (VB) approximation in Bayesian filtering is studied, both as a means to accelerate marginalized particle filtering and as a deterministic local (one-step) approximation. The VB method of approximation is reviewed, together with restrictions that allow various computational savings to be achieved. These variants provide a… (More)

- Emre Özkan, Václav Smídl, Saikat Saha, Christian Lundquist, Fredrik Gustafsson
- Automatica
- 2013

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)

- Václav Smídl, Zdenek Peroutka
- IEEE Trans. Industrial Electronics
- 2012

This paper is concerned with a fixed-point implementation of the extended Kalman filter (EKF) for applications in sensorless control of ac motor drives. The sensitivity of the EKF to round-off errors is well known, and numerically advantageous implementations based on the square-root decomposition of covariance matrices have been developed to address this… (More)

- Saikat Saha, Emre Özkan, Fredrik Gustafsson, Václav Smídl
- 2010 13th International Conference on Information…
- 2010

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 dimensionality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach… (More)

An extension of the AutoRegressive (AR) model is studied, which allows transformations and distortions on the regressor to be handled. Many important signal processing problems are amenable to this Extended AR (i.e., EAR) model. It is shown that Bayesian identification and prediction of the EAR model can be performed recursively, in common with the AR model… (More)

- Václav Smídl, Matej Gasperin
- Proceedings of the 16th International Conference…
- 2013

We present a Rao-Blackwellized point mass filter (RB-PMF) as a deterministic counterpart of the Rao-Blackwellized marginal particle filter (RB-MPF). The main advantage of the proposed filter is its deterministic nature that results in the same estimate for repeated runs over the same data. Moreover, the point mass approximation offers more reliable… (More)

- Václav Smídl, Anthony Quinn
- Computational Statistics & Data Analysis
- 2007

- Václav Šmídl
- 2005

- Václav Smídl, Ondrej Tichy
- ISBI
- 2012

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)

We study Bayesian estimation of the time-varying parameters of a non-stationary AR (autoregressive) process. This is traditionally achieved via exponential forgetting. A numerically tractable solution is available if the forgetting factor is known a priori. This assumption is now relaxed. Instead, we propose joint Bayesian estimation of the AR parameters… (More)