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...

Fault detection and isolation (FDI) logic plays a crucial role in enhancing the safety and reliability, and reducing the operating cost of aircraft propulsion systems. However, it is a challenging problem achieving the FDI task with high reliability. For this purpose, various approaches have been proposed in the literature. In an on-line engine fault diagnoses, two tasks may use Kalman filter t...

In this paper, we present a detailed analysis of the Kalman filter for the application of speech enhancement and identify its shortcomings when the linear predictor model parameters are estimated from speech that has been corrupted with additive noise. We show that when only noise-corrupted speech is available, the poor performance of the Kalman filter may be attributed to the presence of large...

This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...

In the problem of speech enhancement, where a speech signal is corrupted by.In this paper, we propose to combine the Kalman filter with a recent speech enhancement technique, called the phase spectrum compensation procedure, or.we present a class of Kalman filter-based algorithms with some extensions, modifications. These algorithms with alternative speech enhancement algorithms. Conversations ...

In this paper, a homogenous multi-sensor fusion method is used to estimate the trueangular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial MeasurementUnits (IMU). An information form of steady state Kalman filter is designed to fuse the output of four lowaccuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware isimplemen...

In this paper we present a direct Kalman filtering approach for GPS/INS integration. In the approach, GPS and INS non–linearities are preprocessed prior to a Kalman filter. The GPS preprocessed data are taken as measurement input, while the INS preprocessed data are taken as an additional information to the Kalman filter’s state prediction. The advantage of this approach is that a simple and li...

Report [1] compares the Extended Kalman Filter [2, 3, 4], the Iterated Extended Kalman Filter, IEKF, [2, 3, 4] and the Linear Regression Kalman Filter [5] (e.g. the Unscented Kalman Filter, UKF, [6, 7, 8]) on (i) consistency and (ii) information content of their results (estimates and covariance matrices). The nonlinear filter proposed by Bellaire et al. in [9] is not discussed in report [1]. T...

There is a growing interest in using Kalman-filter models in brain modelling. In turn, it is of considerable importance to make Kalman-filters amenable for reinforcement learning. In the usual formulation of optimal control it is computed off-line by solving a backward recursion. In this technical note we show that slight modification of the linear-quadratic-Gaussian Kalman-filter model allows ...

A new filtering algorithm, adaptive square root cubature Kalman filter-Kalman filter (SRCKF-KF) is proposed to reduce the problems of amount of calculation, complex formula-transform, low accuracy, poor convergence or even divergence. The method uses cubature Kalman filter (CKF) to estimate the nonlinear states of model while its linear states are estimated by the Kalman filter (KF). The simula...