Abstract: This paper presents an application of nonlinear filtering techniques for the tracking control design of tracked mobile robot under slip condition. The slip is represented only by the longitudinal wheels slip that is described by just an unknown parameter. The extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF) are used to estimate the states of...

The paper reports about the design of an object tracker which utilizes a family of unscented Kalman filters, one for each tracked object. This is a more efficient design than having one unscented Kalman filter for the family of all moving objects. The performance of the designed and implemented filter is shown by using simulated movements, and also for object movements in 2D an 3D space.

This paper provides a simulation-based approach to filtering and sequential parameter learning for stochastic volatility models. We develop a fast simulation-based approach using the practical filter of Polson, Stroud and Müller (2002). We compare our approach to sequential parameter learning and filtering with an auxiliary particle filtering algorithm based on Storvik (2002). For simulated dat...

A stochastic unscented Kalman filter is designed in an attempt to solve the state estimation problem of the greenhouse climate control systems with missing measurements. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. In order to accommodate the effects of randomly varying arrival of measurement data, the stochastic unscen...

The Kalman filter and its nonlinear variants have been widely used for filtering and state estimation. However, models with severe nonlinearities are not handled well by Kalman filters. Such an application is presented in this paper: the estimation of the overflow losses in a hopper-dredger. The overflow mixture density and flow-rate have to be estimated based on noisy measurements of the total...

In this paper, new attitude determination algorithms using pseudolite signal phase measurements are developed and presented with realistic simulations. Pseudolite signals are used to replace GPS signals which are often not available due to blocking by nearby large structures, for example a crew return vehicle under the International Space Station. A new observation model needs to be applied bec...

The knowledge of the pose and the orientation of a mobile robot in its operating environment is of utmost importance for an autonomous robot. Techniques solving this problem are referred to as self-localization algorithms. Self-localization is a deeply investigated field in mobile robotics, and many effective solutions have been proposed. In this context, Monte Carlo Localization (MCL) is one o...

In this paper, after defining pure proportional navigation guidance in the 3-dimensional state from a new point of view, range estimation for passive homing missiles isexplained. Modeling has been performed by using line of sight coordinates with a particulardefinition. To obtain convergent estimates of those state variables involved particularly inrange channel and unavailable from IR trackers...

The basic problem in Target tracking is to estimate the trajectory of a object from noise corrupted measurements and hence becoming very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filt...

Existing methods of improving particle filters mainly focus on two aspects: designing a good proposal distribution before sampling and allocating particles to a high posterior area after sampling. An auxiliary particle filter (APF) is one such simple algorithm belonging to the former aspect, which generates particles from an importance distribution depending on a more recent observation. Its we...