FastSLAM is a framework which solves the problem of simultaneous localization and mapping using a Rao-Blackwellized particle filter. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling phase. To solve this problem, a FastSLAM method based on particle swarm optimization and unscented particle filter is proposed. The number of pa...

The aim of this paper is to present a novel approach for the design and implementation of an onboard nonlinear state estimation system for an autonomous aerial vehicle. The tasks of such a system include collection of measurement data from different navigation sensors, and estimation of all the quantities describing the system’s state of motion based on the system dynamics and the measurement d...

The paper discusses an adaptive multiuser receiver for CDMA systems in which the scaled unscented filter (SUF) and the square root unscented filter (SURF) are used for joint estimation and tracking of the code delays and multipath coefficients of the received CDMA signals. The proposed channel estimators are more near-far resistant than the conventional extended Kalman filter (EKF) and present ...

This paper presents a real-time driver characterisation algorithm that models the based on 10 parameters governing their control of vehicle longitudinal speed and acceleration. It utilises an open-loop model in conjunction with Unscented Kalman Filter (UKF). The can operate using velocity acceleration measurements, addition to a-priori knowledge path curvature road features such as limit locati...