A new method for the nonlinear transformation of means and covariances in filters and estimators

2000/03/01 by Simon Julier, S. Julier, J. Uhlmann +3 · 44 citations
Computer Science · Engineering · Physics and Astronomy · #Target Tracking and Data Fusion in Sensor Networks #Inertial Sensor and Navigation #Scientific Research and Discoveries

paper · doi:10.1109/9.847726

Abstract

This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parametrize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demonstrated using an example.

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