Unscented filtering and nonlinear estimation

By Simon Justin Julier, Jeffrey K. Uhlmann. 2004

6,883 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Artificial Intelligence

Citations in the corpus

Extended Object Tracking: Introduction, Overview and Applications (2016, methodology): “However, a huge variety of approximate methods has been developed in the past such as the unscented transform [98].”
Model-Based Approaches to Handling Uncertainty (2011, background): “One approach to address these limitations is to use unscented transforms [31].”
Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation [Lecture Notes] (2012, methodology): “RELEVANCE The Kalman filter [2] (and its variants such as the extended Kalman filter [3] and unscented Kalman filter [4]) is one of the most celebrated and popular data fusion algorithms in the field of information processing.”
Stability of consensus extended Kalman filter for distributed state estimation (2016, methodology): “An open problem that deserves further investigation is whether similar, or even stronger, stability properties can be achieved by means of different distributed nonlinear state estimation techniques, for example based on the Unscented Kalman Filter (Julier and Uhlmann, 2004).”
Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature (2007, methodology): “The unscented transformation is founded on intuition [ 30 ]:”
Sparse Gauss-Hermite Quadrature Filter with Application to Spacecraft Attitude Estimation (2011, methodology): “They include the Gauss–Hermite quadrature filter (GHQF) based on the Gauss–Hermite quadrature (GHQ) rule [15,16], the UKF based on the unscented transformation (UT) [6,7], the cubature Kalman filter (CKF) [17], the central difference filter (CDF) [15], and the divided difference filter (DDF) [18].”
Entropy-Based Approach for Uncertainty Propagation of Nonlinear Dynamical Systems (2013, methodology): “are used to determine the set ofK 2n sigma points, whichmake up the symmetric sigma-point set, such that for i ∈ f1; : : : ; ng, each component’s sigma points are given by [8]”
An Overview of Noise-Robust Automatic Speech Recognition (2014, methodology): “2) Unscented Transform: Originally developed to improve the extended Kalman filter and introduced to the field of robust ASR in [215], [216], the unscented transform (UT) [214] gives an accurate estimate of the mean and variance parameters of a Gaussian distribution under a nonlinear transformation ”
Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, methodology): “The updates in (47), (51) and (57) are standard single target measurement updates, and thus can be calculated with wellknown methods (e.g., [22] [23]).”
Rao-Blackwellized particle filter for multiple target tracking (2007, methodology): “Non-linear measurement models can be used by replacing the non-linear model with a locally linearized model as in the extended Kalman filter (EKF) [2,6] or by using the unscented transformation as in the unscented Kalman filter (UKF) [29].”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “Most of the works using the UKF took place at the beginning of the 2000s [37], [38].”
A Numerical-Integration Perspective on Gaussian Filters (2006, methodology): “Recently, an unscented transformation (UT)-based Kalman filter [18] arose and was widely approved as a promising substitute for the EKF [19]–[21].”
Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes (2011, methodology): “To address this issue, an unscented transform [79] is used to approximate the Habitual and Goal-directed Systems in Competition”