This paper is a tutorial survey which focuses on some developments introduction in statistical filtering achieved since the of Wiener and Kalman filters for linear gaussian problems. Kalman are reviewed with filters (including reference to their smoothers and predictors) interesting properties and also their fundamental limitations in nonlinear or unknown environments. For nonlinear filtering p...

The concept of block signal processing is extended to the Kalman filtering problem. Block processing algorithms are developed for stite/signal estimation in noisy linear systems and it is shown that smoothed estimates are naturally obtained. Further, block processing is shown to reduce sensitivity to computational errors, and promises a reduction in the amount of computation, although at a cost...

We discuss the use of low-dimensional physical models of the voice source for speech coding and processing applications. A class of waveform-adaptive dynamic glottal models and parameter tracking procedures are illustrated. The model and analysis procedures are assessed by addressing speech encoding and enhancement, achievable by using a state space version of the dynamical model in a Extended ...

— This paper presents review of techniques and algorithms used for filtering and data association in visual tracking. Kalman filter is an optimal Bayesian filter for linear dynamic models with Gaussian noise. Most of the processes and systems in real world are nonlinear, and in these situations there is extension of Kalman filter named the Extended Kalman filter (EKF). In case when the noise is...

In this paper we describe a new nonlinear estimator for filtering systems with nonlinear process and observation models, based on the optimization with RGO (Restricted Genetic Optimization). Simulation results are used to compare the performance of this method with EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), SNF (Second-order Nonlinear Filter), SIF (Single-stage Iterat...