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Monte Carlo Data Association for Multiple Target Tracking
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.
2001 (English)In: Proceedings of the 2001 IEE International Seminar on Target Tracking: Algorithms and Applications, 2001, 13/1-13/5 p.Conference paper, Published paper (Refereed)
Abstract [en]

The data association problem occurs in multiple target tracking applications. Since nonlinear and non-Gaussian estimation problems are solved approximately in an optimal way using recursive Monte Carlo methods or particle filters, the association step is crucial for the overall performance. We introduce a Bayesian data association method based on the particle filter idea and joint probabilistic data association (JPDA) hypothesis calculations. A comparison with classical EKF based data association methods such as the nearest neighbor (NN) method and the JPDA method is made. The NN association method is also applied to the particle filter method. Multiple target tracking using particle filtering increases the computational burden, therefore a control structure for the number of samples needed is proposed. A radar target tracking application is used in a simulation study for evaluation.

Place, publisher, year, edition, pages
2001. 13/1-13/5 p.
Keyword [en]
Bayes method, Kalman filter, Nonlinear estimation, Radar tracking
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-29618DOI: 10.1049/ic:20010239Local ID: 14997OAI: oai:DiVA.org:liu-29618DiVA: diva2:250435
Conference
IEE International Seminar on Target Tracking: Algorithms and Applications, Enschede, The Netherlands, October, 2001
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-03-29
In thesis
1. Particle filtering for positioning and tracking applications
Open this publication in new window or tab >>Particle filtering for positioning and tracking applications
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A Bayesian approach to positioning and tracking applications naturally leads to a recursive estimation formulation. The recently invented particle filter provides a numerical solution to the non-tractable recursive Bayesian estimation problem. As an alternative, traditional methods such as the extended Kalman filter. which is based on a linearized model and an assumption on Gaussian noise, yield approximate solutions.

In many practical applications, signal quantization and algorithmic complexity are fundamental issues. For measurement quantization, estimation performance is analyzed in detail. The algorithmic complexity is addressed for the marginalized particle filter, where the Kalman filter solves a linear subsystem subject to Gaussian noise efficiently.

The particle filter is adopted to several positioning and tracking applications and compared to traditional approaches. Particularly, the use of external database information to enhance estimation performance is discussed. In parallel, fundamental limits are derived analytically or numerically using the Cramér-Rao lower bound, and the result from estimation studies is compared to the corresponding lower bound. A framework for map-aided positioning at sea is developed, featuring an underwater positioning system using depth information and readings from a sonar sensor and a novel surface navigation system using radar measurements and sea chart information. Bayesian estimation techniques are also used to improve position accuracy for an industrial robot. The bearings-only tracking problem is addressed using Bayesian techniques and map information is used to improve the estimation performance. For multiple-target tracking problems data association is an important issue. A method to incorporate classical association methods when the estimation is based on the particle filter is presented. A real-time implementation of the particle filter as well as hypothesis testing is introduced for a collision avoidance application.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University Electronic Press, 2005. 55 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 924
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-29608 (URN)14987 (Local ID)91-85297-34-8 (ISBN)14987 (Archive number)14987 (OAI)
Public defence
2005-03-18, Sal Visionen, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-11-29Bibliographically approved

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Karlsson, RickardGustafsson, Fredrik

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CiteExportLink to record
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