We report a decentralized, vehicle-based extended information filter designed to enable single-beacon acoustic navigation of multiple underwater vehicles. In single-beacon navigation, ranges from a single reference beacon to a moving underwater vehicle are used in addition to the vehicle’s inertial navigation sensors to perform absolute (as opposed to relative) localization and navigation. For ...

The Kalman filter is the optimal minimum-variance state estimator for linear dynamic systems with Gaussian noise. In addition, the Kalman filter is the optimal linear state estimator for linear dynamic systems with non-Gaussian noise. For nonlinear systems various modifications of the Kalman filter (e.g., the extended Kalman filter, the unscented Kalman filter, and the particle filter) have bee...

The Kalman filter was named after Rudolph E. Kalman published in 1960 his famous paper (Kalman, 1960) describing a recursive solution to the discrete-data linear filtering problem. There are several tutorial papers and books dealing with the subject for a great variety of applications in many areas from engineering to finance (Grewal & Andrews, 2001; Sorenson, 1970; Haykin, 2001; Bar-Shalom & L...

This paper is concerned with the optimal solution of two-stage Kalman filtering for linear discrete-time stochastic time-varying systems with unknown inputs affecting both the system state and the outputs. By means of a newlypresented modified unbiased minimum-variance filter (MUMVF), which appears to be the optimal solution to the addressed problem, the optimality of two-stage Kalman filtering...

The methods of the class of Kalman filters have recently been used in the estimation of the term structure of interest rates. These methods can employ both time-series and cross-sectional aspects of term structure models. This paper compares the performance of two kinds of non-linear Kalman filter algorithms Extended Kalman Filter (EKF) and Square-Root Unscented Kalman Filter (SRUKF) in estimat...

Some issues in markerless tracking of human body motion are addressed. Extended Kalman filters have commonly been applied to kinematic variables, to combine predictions consistent with plausible motion, with the incoming stream of visual measurements. Kalman filtering is applicable only when the underlying distribution is approximately Gaussian. Often, this assumption proves remarkably robust. ...

in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...