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

Thies  de Boer; Max Ian Schöpe; Hans Driessen

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

Thies de Boer, Max Ian Schöpe, Hans Driessen

Microwave Sensing, Signals & Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-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 language	English
Title of host publication	2021 IEEE 24th International Conference on Information Fusion (FUSION)
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	1-8
Number of pages	8
ISBN (Electronic)	978-1-7377497-1-4
ISBN (Print)	978-1-6654-1427-2
Publication status	Published - 2021
Event	2021 IEEE 24th International Conference on Information Fusion (FUSION) - Hybrid at Sun City, South Africa Duration: 1 Nov 2021 → 4 Nov 2021 Conference number: 24th

Conference

Conference	2021 IEEE 24th International Conference on Information Fusion (FUSION)
Abbreviated title	Fusion 2021
Country/Territory	South Africa
City	Hybrid at Sun City
Period	1/11/21 → 4/11/21

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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Radar_Resource_Management_for_Multi-Target_Tracking_Using_Model_Predictive_ControlFinal published version, 442 KB

https://ieeexplore.ieee.org/document/9626897

Cite this

@inproceedings{2ead538797aa4ecd839b21704388cad6,

title = "Radar Resource Management for Multi-Target Tracking Using Model Predictive Control",

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.",

keywords = "Radar Resource Management, Constrained Optimization, Partially Observable Markov Decision Process, Model Predictive Control",

author = "{de Boer}, Thies and Sch{\"o}pe, {Max Ian} and Hans Driessen",

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.; 2021 IEEE 24th International Conference on Information Fusion (FUSION), Fusion 2021 ; Conference date: 01-11-2021 Through 04-11-2021",

year = "2021",

language = "English",

isbn = "978-1-6654-1427-2",

pages = "1--8",

booktitle = "2021 IEEE 24th International Conference on Information Fusion (FUSION)",

publisher = "IEEE",

address = "United States",

}

de Boer, T, Schöpe, MI & Driessen, H 2021, Radar Resource Management for Multi-Target Tracking Using Model Predictive Control. in 2021 IEEE 24th International Conference on Information Fusion (FUSION): Proceedings., 9626897, IEEE, pp. 1-8, 2021 IEEE 24th International Conference on Information Fusion (FUSION), Hybrid at Sun City, South Africa, 1/11/21. <https://ieeexplore.ieee.org/document/9626897>

Radar Resource Management for Multi-Target Tracking Using Model Predictive Control. / de Boer, Thies ; Schöpe, Max Ian; Driessen, Hans.
2021 IEEE 24th International Conference on Information Fusion (FUSION): Proceedings. IEEE, 2021. p. 1-8 9626897.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Radar Resource Management for Multi-Target Tracking Using Model Predictive Control

AU - de Boer, Thies

AU - Schöpe, Max Ian

AU - Driessen, Hans

N1 - Conference code: 24th

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Radar Resource Management

KW - Constrained Optimization

KW - Partially Observable Markov Decision Process

KW - Model Predictive Control

M3 - Conference contribution

SN - 978-1-6654-1427-2

SP - 1

EP - 8

BT - 2021 IEEE 24th International Conference on Information Fusion (FUSION)

PB - IEEE

T2 - 2021 IEEE 24th International Conference on Information Fusion (FUSION)

Y2 - 1 November 2021 through 4 November 2021

ER -

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

Abstract

Conference

Bibliographical note

Keywords

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Cite this