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.
Keywords
Kalman filterExtended Kalman filterLinearizationNonlinear systemUnscented transformCovarianceEstimationTransformation (genetics)Computer scienceInvariant extended Kalman filterControl theory (sociology)Scale (ratio)AlgorithmMathematicsArtificial intelligenceEngineeringStatisticsControl (management)Geography
Affiliated Institutions
Related Publications
New extension of the Kalman filter to nonlinear systems
The Kalman Filter (KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the applicatio...
The square-root unscented Kalman filter for state and parameter-estimation
Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include ...
The scaled unscented transformation
This paper describes a generalisation of the unscented transformation (UT) which allows sigma points to be scaled to an arbitrary dimension. The UT is a method for predicting me...
Statistically Linearized Estimation of Reentry Trajectories
Several filters are applied to the problem of state estimation from inertial measurements of reentry drag. This is a highly nonlinear problem of practical significance. It is fo...
The higher order unscented filter
This article proposes a technique for the selection of the /spl sigma/-set for a probability distribution approximation filter, i.e., the unscented filter. The /spl sigma/-set i...
Publication Info
- Year
- 2004
- Type
- article
- Volume
- 92
- Issue
- 3
- Pages
- 401-422
- Citations
- 6286
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
6286
OpenAlex
Cite This
Simon Julier,
Jeffrey Uhlmann
(2004).
Unscented Filtering and Nonlinear Estimation.
Proceedings of the IEEE
, 92
(3)
, 401-422.
https://doi.org/10.1109/jproc.2003.823141
Identifiers
- DOI
- 10.1109/jproc.2003.823141