An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means of Kalman smoother is presented. The main advantage of the Kalman smoother approach compared to other adaptive algorithms such as LMS or RLS is that the tracking lag can be avoided. This advantage is clearly presented with simulations. Kalman smoother is also applied to tracking of alpha band characteristic...

we compare performance of adaptive schemes which are based on radial-basis functions and kalman filters for fast extraction of auditory evoked potentials. moreover, we propose a new method based on evoked potential modeling in the kalman filter framework, which can improve the accuracy compared to the existing methods. simulation results show that adaptive schemes and the kalman method are not ...

An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means of Kalman smoother is presented. The main advantage of the Kalman smoother approach compared to other adaptive algorithms such as LMS or RLS is that the tracking lag can be avoided. This advantage is clearly presented with simulations. Kalman smoother is also applied to tracking of alpha band characteristic...

The paper deals with Kalman or H2 smoothing problem for wireless sensor networks WSNs with multiplicative noises. Packet loss occurs in the observation equations, and multiplicative noises occur both in the system state equation and the observation equations. The Kalman smoothers which include Kalman fixed-interval smoother, Kalman fixedlag smoother, and Kalman fixed-point smoother are given by...

Kalman smoothers obtain state estimates in a system with stochastic dynamics and measurement noise. We consider the smoothing problem in a distributed setting, present a cooperative smoothing algorithm for Gauss-Markov linear models, and provide a convergence analysis for the algorithm. An extension of the algorithm that maximizes the likelihood with respect to a sequence of state vectors subje...

An improved Kalman Smoother for atmospheric inversions L. M. P. Bruhwiler, A. M. Michalak, W. Peters, D. F. Baker, and P. Tans NOAA Climate Monitoring and Diagnostics Laboratory, Boulder, Colorado, USA Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, ...

Abstract In this note we outline the derivation of the variational Kalman smoother, in the context of Bayesian Linear Dynamical Systems. The smoother is an efficient algorithm for the E-step in the ExpectationMaximisation (EM) algorithm for linear-Gaussian state-space models. However, inference approximations are required if we hold distributions over parameters. We derive the E-step updates fo...

The scheme to propagate correlations between online and off-line state variables in atmospheric inversions using the fixed-lag Kalman smoother proposed in Bruhwiler et al. (2005) is explained as a process to impose a balanced constraint on the on-line state variables. It is then extended to the fixed-lag ensemble square root Kalman smoother and fixed-lag square root sigma-point Kalman smoother,...