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EKF/UKF Maneuvering Target Tracking using Coordinated Turn Models with Polar/Cartesian Velocity
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2014 (English)In: 17th International Conference on Information Fusion (FUSION), 2014, Institute of Electrical and Electronics Engineers (IEEE), 2014, 1-8 p.Conference paper, Published paper (Refereed)
Abstract [en]

Nonlinear Kalman filter adaptations such as extended Kalman filters (EKF) or unscented Kalman filters (UKF) provide approximate solutions to state estimation problems in nonlinear models. The algorithms utilize mean values and covariance matrices to represent the probability densities in the otherwise intractable Bayesian filtering equations. As a consequence, their estimation performance can show significant dependence on the choice of state coordinates. The here considered problem of tracking maneuvering targets using coordinated turn (CT) models is one practically relevant example: The velocity in the target state can either be formulated in Cartesian or polar coordinates. We extend a previous study to a broader range of CT models that allow for changes in target speed and turn rate, and investigate UKF as well as EKF variants in terms of their performance and sensitivity to noise parameters. The results advocate for the use of polar CT models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2014. 1-8 p.
Keyword [en]
Coordinated turn model; Maneuvering target tracking
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-108957ISI: 000363896100153ISBN: 978-849012355-3 (print)OAI: oai:DiVA.org:liu-108957DiVA: diva2:734112
Conference
17th International Conference on Information Fusion, Salamanca, Spain, July 7-10, 2014
Funder
Security LinkSwedish Foundation for Strategic Research
Available from: 2014-07-14 Created: 2014-07-14 Last updated: 2017-02-27
In thesis
1. Advanced Kalman Filtering Approaches to Bayesian State Estimation
Open this publication in new window or tab >>Advanced Kalman Filtering Approaches to Bayesian State Estimation
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Bayesian state estimation is a flexible framework to address relevant problems at the heart of existing and upcoming technologies. Application examples are obstacle tracking for driverless cars and indoor navigation using smartphone sensor data. Unfortunately, the mathematical solutions of the underlying theory cannot be translated to computer code in general. Therefore, this thesis discusses algorithms and approximations that are related to the Kalman filter (KF).

Four scientific articles and an introduction with the relevant background on Bayesian state estimation theory and algorithms are included. Two articles discuss nonlinear Kalman filters, which employ the KF measurement update in nonlinear models. The numerous variants are presented in a common framework and the employed moment approximations are analyzed. Furthermore, their application to target tracking problems is discussed. A third article analyzes the ensemble Kalman filter (EnKF), a Monte Carlo implementation of the KF that has been developed for high-dimensional geoscientific filtering problems. The EnKF is presented in a simple KF framework, including its challenges, important extensions, and relations to other filters. Whereas the aforementioned articles contribute to the understanding of existing algorithms, a fourth article devises novel filters and smoothers to address heavy-tailed noise. The development is based on Student’s t distribution and provides simple recursions in the spirit of the KF. The introduction and articles are accompanied by extensive simulation experiments.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 81 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1832
National Category
Signal Processing Control Engineering Computational Mathematics Computer Science Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-134867 (URN)10.3384/diss.diva-134867 (DOI)9789176855782 (ISBN)
Public defence
2017-04-21, Visionen, B-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2017-03-22 Created: 2017-02-27 Last updated: 2017-03-22Bibliographically approved

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Roth, MichaelHendeby, GustafGustafsson, Fredrik

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