In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [13]. Various filtering methods – Particle Filtering, and derived Kalman Filter methods (Extended, Unscented) – have been applied success...

This paper addresses the problem of integration of Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) for the purpose of developing a low-cost, robust and highly accurate navigation system for unmanned surface vehicles (USVs). A tightly-coupled integration approach is one of the most promising architectures to fuse the GNSS data with INS measurements. However, the re...

Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering ...

Particle filtering is a sequential Monte Carlo method [3] that uses importance sampling to draw samples from probability distributions. In a particle filter the target state is represented by a point mass particle set that is propagated and updated using conditional probability representations of the motion model and measurement model. Methods that improve the sampling efficiency include [3] re...

Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at same time. However, it poses challenge since not all quantities interest can be directly measured due cost and/or technological reasons. Therefore, combining principle robust filtering and unscented particle algorithm, filter estimation method state proposed es...

This paper gives the survey of the existing developments of Visual object target tracking using particle filter from the last decade and discusses the advantage and disadvantages of various particle filters. A variety of different approaches and algorithms have been proposed in literature. At present most of the work in Visual Object Target Tracking is focusing on using particle filter. The par...

In INS (Inertial Navigation System) /GPS (Global Positioning System) integration, nonlinear models should be properly handled. The most popular and commonly used method is the Extended Kalman Filter (EKF) which approximates the nonlinear state and measurement equations using the first order Taylor series expansion. On the other hand, recently, some nonlinear filtering methods such as Gaussian S...