In this work we propose a new filtering approach for linear discrete time non-Gaussian systems that generalizes a previous result concerning quadratic filtering [A. De Santis, A. Germani, and M. Raimondi, IEEE Trans. Automat. Control, 40 (1995) pp. 1274–1278]. A recursive νth-order polynomial estimate of finite memory 1 is achieved by defining a suitable extended state which allows one to solve...

This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative.

Filtering Techniques for Pose Estimation With Applications to Unmanned Air Vehicles Bryce Benson Ready Department of Electrical and Computer Engineering Doctor of Philosophy This work presents two novel methods of estimating the state of a dynamic system in a Kalman Filtering framework. The first is an application specific method for use with systems performing Visual Odometry in a mostly plana...

Noise reduction for speech signals has applications ranging from speech enhancement for cellular communications, to front ends for speech recognition systems. A neural network based time-domain method called Dual Extended Kalman Filtering (Dual EKF) is presented for removing nonstationary and colored noise from speech. This paperdescribes the algorithm and provides a set of experimental results.

Bad data may lead to performance degradation or even instability of a power system, which can be caused by various factors: unintentional PMU abnormalities, topology error, malicious cyber-attacks, electromagnetic interference, temporary loss communication links, external disturbances, extraneous noise biases, etc. In order develop more resilient and reliable state estimation technique, this ma...