This chapter presents a unified probabilistic and algorithmic framework for nonlinear dual
estimation methods. Also included is a brief review of the EKF for both state and weight …

Dual estimation refers to the problem of simultaneously estimating the state of a dynamic
system and the model which gives rise to the dynam (cid: 173) ics. Algorithms include …

Numerous applications require either the estimation or prediction of a noisy time-series.
Examples include speech enhancement, economic forecasting, and geophysical modeling …

This chapter discusses the underlying assumptions and flaws in the EKF, and presents an
alternative filter with performance superior to that of the EKF: the unscented Kalman filter …

Prediction, estimation, and smoothing are fundamental to signal processing. To perform
these interrelated tasks given noisy data, we form a time series model of the process that …

This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an
improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman (1997). A …

Probabilistic inference is the problem of estimating the hidden variables (states or
parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete …

The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for
nonlinear systems. However, more than 35 years of experience in the estimation community …

State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-
contained book consists of seven chapters by expert contributors that discuss Kalman …

The extended Kalman filter (EKF) is considered one of the most effective methods for both
nonlinear state estimation and parameter estimation (eg, learning the weights of a neural …