Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability

Peter Svenningsson; Nicolas Kruse; Francesco  Fioranelli; Alexander Yarovoy

doi:10.1109/RadarConf2248738.2022.9764292

Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability

Peter Svenningsson, Nicolas Kruse, Francesco Fioranelli, Alexander Yarovoy

Microwave Sensing, Signals & Systems

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

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Abstract

Cognitive radar frameworks rely on the ability to quantify and reason on future uncertainty, which allows for the selection of an optimal decision policy. These methods require that the uncertainty estimates provided by the underlying statistical model are well-calibrated, i.e. consistent with true uncertainty. In this work, the utilization of probability calibration techniques for target classification is explored. It is shown from simulations and experimental data that the proposed techniques can be used to correct errors in uncertainty estimates caused by incorrect modeling assumptions, such as the independence of sensors and the independence of classification covariates. This correction improves classification performance and the reliability of cognitive systems so that resources are utilized in accordance with user-defined cost functions.

Original language	English
Title of host publication	2022 IEEE Radar Conference (RadarConf22) Proceedings
Place of Publication	Piscataway
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-7281-5368-1
ISBN (Print)	978-1-7281-5369-8
DOIs	https://doi.org/10.1109/RadarConf2248738.2022.9764292
Publication status	Published - 2022
Event	2022 IEEE Radar Conference - New York City, United States Duration: 21 Mar 2022 → 25 Mar 2022

Publication series

Name	Proceedings of the IEEE Radar Conference
ISSN (Print)	1097-5764

Conference

Conference	2022 IEEE Radar Conference
Abbreviated title	RadarConf22
Country/Territory	United States
City	New York City
Period	21/03/22 → 25/03/22

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

Cognitive radar
probability calibration
resource management
target classification

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10.1109/RadarConf2248738.2022.9764292

Calibration_of_Cognitive_Classification_Systems_for_Radar_Networks_for_Increased_ReliabilityFinal published version, 913 KB

Cite this

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title = "Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability",

abstract = "Cognitive radar frameworks rely on the ability to quantify and reason on future uncertainty, which allows for the selection of an optimal decision policy. These methods require that the uncertainty estimates provided by the underlying statistical model are well-calibrated, i.e. consistent with true uncertainty. In this work, the utilization of probability calibration techniques for target classification is explored. It is shown from simulations and experimental data that the proposed techniques can be used to correct errors in uncertainty estimates caused by incorrect modeling assumptions, such as the independence of sensors and the independence of classification covariates. This correction improves classification performance and the reliability of cognitive systems so that resources are utilized in accordance with user-defined cost functions.",

keywords = "Cognitive radar, probability calibration, resource management, target classification",

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year = "2022",

doi = "10.1109/RadarConf2248738.2022.9764292",

language = "English",

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Svenningsson, P, Kruse, N , Fioranelli, F & Yarovoy, A 2022, Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability. in 2022 IEEE Radar Conference (RadarConf22) Proceedings., 9764292, Proceedings of the IEEE Radar Conference, IEEE, Piscataway, 2022 IEEE Radar Conference
, New York City, United States, 21/03/22. https://doi.org/10.1109/RadarConf2248738.2022.9764292

Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability. / Svenningsson, Peter; Kruse, Nicolas ; Fioranelli, Francesco et al.
2022 IEEE Radar Conference (RadarConf22) Proceedings. Piscataway: IEEE, 2022. 9764292 (Proceedings of the IEEE Radar Conference).

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

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AU - Fioranelli, Francesco

AU - Yarovoy, Alexander

N1 - 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.

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N2 - Cognitive radar frameworks rely on the ability to quantify and reason on future uncertainty, which allows for the selection of an optimal decision policy. These methods require that the uncertainty estimates provided by the underlying statistical model are well-calibrated, i.e. consistent with true uncertainty. In this work, the utilization of probability calibration techniques for target classification is explored. It is shown from simulations and experimental data that the proposed techniques can be used to correct errors in uncertainty estimates caused by incorrect modeling assumptions, such as the independence of sensors and the independence of classification covariates. This correction improves classification performance and the reliability of cognitive systems so that resources are utilized in accordance with user-defined cost functions.

AB - Cognitive radar frameworks rely on the ability to quantify and reason on future uncertainty, which allows for the selection of an optimal decision policy. These methods require that the uncertainty estimates provided by the underlying statistical model are well-calibrated, i.e. consistent with true uncertainty. In this work, the utilization of probability calibration techniques for target classification is explored. It is shown from simulations and experimental data that the proposed techniques can be used to correct errors in uncertainty estimates caused by incorrect modeling assumptions, such as the independence of sensors and the independence of classification covariates. This correction improves classification performance and the reliability of cognitive systems so that resources are utilized in accordance with user-defined cost functions.

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M3 - Conference contribution

SN - 978-1-7281-5369-8

T3 - Proceedings of the IEEE Radar Conference

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Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability

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Keywords

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