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Estimating Polynomial Structures from Radar Data
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
2010 (English)In: Proceedings of the 13th International Conference on Information Fusion, Edinburgh, Scotland, 2010Conference paper, Published paper (Refereed)
Abstract [en]

Situation awareness for vehicular safety and autonomy functions includes knowledge of the drivable area. This area is normally constrained between stationary road-side objects as guard-rails, curbs, ditches and vegetation. We consider these as extended objects modeled by polynomials along the road, and propose an algorithm to track each polynomial based on noisy range and bearing detections, typically from a radar. A straightforward Kalman filter formulation of the problem suffers from the errors-in-variables (EIV) problem in that the noise enters the system model. We propose an EIV modification of the Kalman filter and demonstrates its usefulness using radar data from public roads.

Place, publisher, year, edition, pages
Edinburgh, Scotland, 2010.
Keyword [en]
Extended object, Extended target tracking, Polynomial, Errors in output, Errors in variables, Automotive radar, Road map
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-62844ISBN: 978-0-9824438-1-1 (print)OAI: oai:DiVA.org:liu-62844DiVA: diva2:374824
Conference
13th International Conference on Information Fusion, Edinburgh, United Kingdom, 26-29 July, 2010
Projects
SEFSCadics
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Available from: 2011-12-16 Created: 2010-12-06 Last updated: 2013-09-17Bibliographically approved

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