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Random Set Methods: Estimation of Multiple Extended Objects
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-3450-988X
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.
Department of Electrical and Electronics Engineering, Middle East Technical University.
2014 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 21, no 2, 73-82 p.Article in journal (Refereed) Published
Abstract [en]

Random set based methods have provided a rigorous Bayesian framework and have been used extensively in the last decade for point object estimation. In this paper, we emphasize that the same methodology offers an equally powerful approach to estimation of so called extended objects, i.e., objects that result in multiple detections on the sensor side. Building upon the analogy between Bayesian state estimation of a single object and random finite set estimation for multiple objects, we give a tutorial on random set methods with an emphasis on multiple extended object estimation. The capabilities are illustrated on a simple yet insightful real life example with laser range data containing several occlusions.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2014. Vol. 21, no 2, 73-82 p.
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-105530DOI: 10.1109/MRA.2013.2283185ISI: 000337124700012OAI: oai:DiVA.org:liu-105530DiVA: diva2:708079
Projects
CADICSCUAS
Funder
Linnaeus research environment CADICS
Available from: 2014-03-26 Created: 2014-03-26 Last updated: 2017-12-05Bibliographically approved

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Granström, KarlLundquist, ChristianGustafsson, Fredrik
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