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Range Estimation using Angle-Only Target Tracking with Particle Filters
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 American Control Conference, 2001, 3743-3748 vol.5 p.Conference paper, Published paper (Refereed)
Abstract [en]

We consider the recursive state estimation of a maneuverable aircraft using an airborne passive IR-sensor. The main issue addressed in the paper is the range- and velocity estimation using angle-only measurements. 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 angle-only target tracking problem. These Monte Carlo filters approximate optimal inference by simulating a large number of tracks, or particles. In a simulation study our particle filter approach is compared to a range parameterized extended Kalman filter (RPEKF). Tracking is performed in both Cartesian and modified spherical coordinates (MSC).

Place, publisher, year, edition, pages
2001. 3743-3748 vol.5 p.
Keyword [en]
Angle-only tracking, Particle filters, Passive sensors, RPEKF
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-29617DOI: 10.1109/ACC.2001.946218Local ID: 14996ISBN: 0-7803-6495-3 (print)OAI: oai:DiVA.org:liu-29617DiVA: diva2:250434
Conference
2001 American Control Conference, Arlington, VA, USA, June, 2001
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-04-16

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Karlsson, RickardGustafsson, Fredrik

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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
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