International Journal of Intelligent Mechatronics and Robotics, 3(2), 55-66, April-June 2013 55 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. ABSTRACT This paper presents a new robust adaptive unscented particle fltering algorithm by adopting the concept of robust adaptive fltering to the unscented particle flter. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular obser- vations and the kinematic model noise. It also uses the unscented transformation to improve the accuracy of particle fltering, thus providing the reliable state estimation for improving the performance of robust adaptive fltering. Experiments and comparison analysis demonstrate that the proposed fltering algorithm can effectively resist disturbances due to system state noise and observation noise, leading to the improved fltering accuracy. Robust Adaptive Unscented Particle Filter Li Xue, School of Automatics, Northwestern Polytechnical University, Xi’an, China Shesheng Gao, School of Automatics, Northwestern Polytechnical University, Xi’an, China Yongmin Zhong, School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Bundoora, VIC, Australia Keywords: Adaptive Factor, Equivalent Weight Function, Particle Filter, Robust Adaptive Filtering, Unscented Particle Filter, Unscented Transformation 1. INTRODUCTION The problem of nonlinear filtering is common in many areas such as integrated navigation system, geodetic positioning, automatic control, information fusion and signal processing. The extended Kalman filtering is a commonly used filtering method to nonlinear systems (Julier, Uhlmann & Durrant-Whyte, 2000; Lefebvre, Bruyninckx & Schutter, 2004). This is an approximation method, in which nonlinear system equations are linearized by the Taylor expansion and the linearized states are as- sumed to obey the Gaussian distribution. The linearization stage of the state equations may lead to a problem of divergence or instability (Simon, 2006). Especially, when the practical probability function has multiple peak values, the estimated state error is very large or even divergent (Grewal & Andrews, 2008). The unscented Kalman filtering (UKF) method combines the concept of unscented transform with the Kalman filtering (Julier & Uhlmann, 2004; Wan & van der Merwe, 2000). This method inherits the linear update structure of the Kalman filtering. It uses only the second-order system moments, which may not be sufficient for some nonlinear systems. The particle filtering (PF) is an optimal recursive Bayesian filtering method based on DOI: 10.4018/ijimr.2013040104