Topic
Alpha beta filter
About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: Three ''5- block'' forms of the extended DKF (5-block EDKF) are derived as globally optimal state estimators in the sense that the first two filters are equivalent to the recently developed extended recursive three-step filter and the third is equivalent toThe conventional augmented state Kalman filter.
26 citations
11 Nov 2010
TL;DR: This paper presents a new method to estimate dynamic neural activity from EEG signals based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account.
Abstract: This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.
26 citations
TL;DR: In this paper, a new approach to two filter smoothing formulae via diagonalization of the general time variant hamiltonian equations of the linear estimation problem is presented, which shows the special role of the famous Mayno-Fraser two filter formula and also provides insight into certaini nvariance properties of backwards Kalman filter estimates.
Abstract: We present a new approach to two filter smoothing formulae via diagonalization of the general time variant hamiltonian equations of the linear estimation problem. This approach shows the special role of the famous Mayno-Fraser two filter formulae and also provides insight into certaini nvariance properties of backwards Kalman filter estimates.
26 citations
TL;DR: This work applies the nonlinear method of unscented Kalman filters (UKFs) to observe states and estimate parameters in a neural mass model that can simulate distinct rhythms in electroencephalography (EEG) including dynamical evolution during epilepsy seizures.
Abstract: Recent progress in Kalman filters to estimate states and parameters in nonlinear systems has provided the possibility of applying such approaches to neural systems. We here apply the nonlinear method of unscented Kalman filters (UKFs) to observe states and estimate parameters in a neural mass model that can simulate distinct rhythms in electroencephalography (EEG) including dynamical evolution during epilepsy seizures. We demonstrate the efficiency of the UKF in estimating states and parameters. We also develop an UKF-based control strategy to modulate the dynamics of the neural mass model. In this strategy the UKF plays the role of observing states, and the control law is constructed via the estimated states. We demonstrate the feasibility of using such a strategy to suppress epileptiform spikes in the neural mass model.
26 citations
TL;DR: In this paper, a Luenberger-type boundary observer is presented for a class of distributed-parameter systems described by time-varying linear hyperbolic partial integro-differential equations and it is shown that the backstepping method can be employed without severe limitations on the regularity of the time- varying terms.
26 citations