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Informative Path Planning for Active Tracking of Agile Targets
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.ORCID iD: 0000-0002-4671-3239
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6957-2603
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Faculty of Science & Engineering. Linköping University.ORCID iD: 0000-0002-1971-4295
2019 (English)In: 2019 IEEE Aerospace Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1-11, article id 06.0701Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a method to generate informative trajectories for a mobile sensor that tracks agile targets.The goal is to generate a sensor trajectory that maximizes the tracking performance, captured by a measure of the covariance matrix of the target state estimate. The considered problem is acombination of estimation and control, and is often referred to as informative path planning (IPP). When using nonlinear sensors, the tracking performance depends on the actual measurements, which are naturally unavailable in the planning stage.The planning problem hence becomes a stochastic optimization problem, where the expected tracking performance is used inthe objective function. The main contribution of this work is anapproximation of the problem based on deterministic sampling of the predicted target distribution. This is in contrast to prior work, where only the most likely target trajectory is considered.It is shown that the proposed method greatly improves the ability to track agile targets, compared to a baseline approach.   

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 1-11, article id 06.0701
Keywords [en]
Informative Path Planning; Target Tracking; Sensor Management; Stochastic Control
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-155035DOI: 10.1109/AERO.2019.8741840ISBN: 9781538668542 (electronic)ISBN: 9781538668559 (print)OAI: oai:DiVA.org:liu-155035DiVA, id: diva2:1295062
Conference
Proceedings of 2019 IEEE Aerospace Conference, Big Sky, MT, USA, March 3-8, 2019
Projects
WASP
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-03-09 Created: 2019-03-09 Last updated: 2019-08-12Bibliographically approved

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Axehill, Daniel

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CiteExportLink to record
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