Unscented Filtering and Nonlinear Estimation

2004/03/01 by Simon Julier, S.J. Julier, J.K. Uhlmann +1 · 6,503 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Computer science #Control (management) #Control theory (sociology) #Covariance #Engineering #Estimation #Extended Kalman filter #GNSS positioning and interference #Geography #Inertial Sensor and Navigation #Invariant extended Kalman filter #Kalman filter #Linearization #Mathematics #Nonlinear system #Scale (ratio) #Statistics #Target Tracking and Data Fusion in Sensor Networks #Transformation (genetics) #Unscented transform

paper · doi:10.1109/jproc.2003.823141

published in Proceedings of the IEEE 92(3), 401-422 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2004/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.

Citations

• Dynamical equations for optimal nonlinear filtering
• A new method for the nonlinear transformation of means and covariances in filters and estimators

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Related

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• Quadratic Extended and Unscented Kalman Filter Updates
• A new method for the nonlinear transformation of means and covariances in filters and estimators
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