A new approach for filtering nonlinear systems

Abstract

In this paper we describe a new recursive lin- ear estimator for filtering systems with nonlinear process and observation models. This method uses a new parameterisa- tion of the mean and covariance which can be transformed directly by the system equations to give predictions of the transformed mean and covariance. We show that this tech- nique is more accurate and far easier to implement than an extended Kalman filter. Specifically, we present empirical results for the application of the new filter to the highly non- linear kinematics of maneuvering vehicles.