Radar Resource Management for Multi-Target Tracking Using Model Predictive Control

Thies de Boer, Max Ian Schöpe, Hans Driessen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

The radar resource management problem in a multi-target tracking scenario is considered. Partially observable Markov decision processes (POMDPs) are used to describe each tracking task. Model predictive control is applied to solve the POMDPs in a non-myopic way. As a result, the computational complexity compared to stochastic optimization methods such as policy rollout is dramatically reduced while the resource allocation results maintain similar. This is shown through simulations of dynamic multi-target tracking scenarios in which the cost and computational complexity of different approaches are compared.
Original languageEnglish
Title of host publication2021 IEEE 24th International Conference on Information Fusion (FUSION)
Subtitle of host publicationProceedings
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-7377497-1-4
ISBN (Print)978-1-6654-1427-2
Publication statusPublished - 2021
Event2021 IEEE 24th International Conference on Information Fusion (FUSION) - Hybrid at Sun City, South Africa
Duration: 1 Nov 20214 Nov 2021
Conference number: 24th

Conference

Conference2021 IEEE 24th International Conference on Information Fusion (FUSION)
Abbreviated titleFusion 2021
Country/TerritorySouth Africa
CityHybrid at Sun City
Period1/11/214/11/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Radar Resource Management
  • Constrained Optimization
  • Partially Observable Markov Decision Process
  • Model Predictive Control

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