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Auxiliary Particle Filters for Tracking a Maneuvering Target
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.
2000 (English)In: Proceedings of the 39th IEEE Conference on Decision and Control, IEEE , 2000, 3891-3895 vol.4 p.Conference paper, Published paper (Refereed)
Abstract [en]

We consider the recursive state estimation of a highly maneuverable target. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the maneuvering target tracking problem. These Monte Carlo filters perform optimal inference by simulating a large number of tracks, or particles. Each particle is assigned a probability weight determined by its likelihood. The maina dvantage of our approach is that linearizations and Gaussian assumptions need not be considered. Instead, a nonlinear model is directly used during the prediction and likelihood update. Detailed nonlinear dynamics models and non-Gaussian sensors can therefore be utilized in an optimal manner resulting in high performance gains. In a simulation comparison with current state-of-the-art tracking algorithms we show that our approach yields performance improvements. Moreover, incorporation of physical constraints with sustained optimal performance is straight forward, which is virtually impossible to incorporate for linear Gaussian filters. With the particle filtering approach we advocate these constraints are easily introduced and improve the results.

Place, publisher, year, edition, pages
IEEE , 2000. 3891-3895 vol.4 p.
Keyword [en]
Bayes methods, Filtering theory, Probability, State estimation, Target tracking
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-29619DOI: 10.1109/CDC.2000.912320Local ID: 14998ISBN: 0-7803-6638-7 (print)OAI: oai:DiVA.org:liu-29619DiVA: diva2:250436
Conference
39th IEEE Conference on Decision and Control, Sydney, Australia, 12-15 December, 2000
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2014-12-17
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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CiteExportLink to record
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