We investigate the problem of tracking a moving source in shallow ocean in a Bayesian framework, using acoustic field measurements which are more informative than the commonly employed bearings-only or time-delay measurements. The acoustic field measurement model is described and compared with the bearings-only measurement model. A general approach to Bayesian filtering based on the Gaussian ap...

In this paper different filtering techniques for nonlinear state estimation are explored and compared. We distinguish between approaches that approximate the nonlinear function (extended Kalman filter) and other approaches approximating the distribution of measurements and state (unscented Kalman filter and sequential Monte Carlo filter). The paper is showing both, the algorithms and simulated ...

Distributed linear estimation theory has received increased attention in recent years due to several promising industrial applications. Distributed nonlinear estimation, however is still a relatively unexplored field despite the need in numerous practical situations for techniques that can handle nonlinearities. This paper presents a unified way of describing distributed implementations of thre...

We introduce the Unscented von Mises–Fisher Filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the n-dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, for example when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, ...

Contraction theory entails a theoretical framework in which convergence of a nonlinear system can be analysed differentially in an appropriate contraction metric. This paper is concerned with utilizing stochastic contraction theory to conclude on exponential convergence of the Unscented Kalman-Bucy Filter. The underlying process and measurement models of interest are Itô-type stochastic differe...

By monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter (FAUKF)-based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong trac...

Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended ...

This paper describes a quaternion implementation of an Unscented Kalman Filter for attitude estimation on CubeSats using measurements of a sun vector, a magnetic field vector and angular velocity. For faster convergence of the attitude estimate, a SVD-method solving Wahba’s problem has been proposed, which provides an initial attitude estimate. Using unit quaternions provides a singularity free...

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an ...

A modification scheme to the ensemble Kalman filter (EnKF) is introduced based on the concept of the unscented transform (Julier et al., 2000; Julier and Uhlmann, 2004), which therefore will be called the ensemble unscented Kalman filter (EnUKF) in this work. When the error distribution of the analysis is symmetric (not necessarily Gaussian), it can be shown that, compared to the ordinary EnKF,...