Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of predictive and posterior probability density function (pdf). This paper introduces a filter called adaptive kernel Kalman (AKKF). The AKKF approximates arbitrary pdf hidden states using mean embedding (KME) reproducing Hilbert space (RKHS). In parallel with KME, some particles data are used to capture ...

-Target tracking is one of the major aspects often used in sonar applications, surveillance systems, communication systems, embedded applications etc. To obtain kinematic components of a moving target such as position, velocity, and acceleration, one of the most used approaches in target tracking is stochastic estimation approach. Movement of the target is described by state space dynamic syste...

This paper presents two recursive schemes for current reference generation for shunt active filters under unknown fundamental frequency. The schemes are based on the liner Kalman filter that needs the knowledge of the fundamental frequency. In practice, the fundamental frequency of the power system grid can vary. If it differs from the fundamental frequency considered in the mathematical model ...

Abstract This study investigates the empirical validity of the variability hypothesis in Turkey for the period of February 2005-November 2015, by using cross-sectional relative price data and by focusing on the assumptions of linearity and stability. The linearity assumption between the two variables is ensured by estimating quadratic regression equation. The assumption of stability is secur...

Preface These lecture notes are concerned with state estimation problem of linear and particularly nonlinear discrete and continuous-discrete stochastic systems. State estimation has a great variety of applications including The general solution of the state estimation problem is based on the Bayesian recursive relations and the Fokker-Planck equation which generate conditional probability dens...