Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability

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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 languageEnglish
Title of host publication2022 IEEE Radar Conference (RadarConf22) Proceedings
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)978-1-7281-5368-1
ISBN (Print)978-1-7281-5369-8
Publication statusPublished - 2022
Event2022 IEEE Radar Conference
- New York City, United States
Duration: 21 Mar 202225 Mar 2022

Publication series

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


Conference2022 IEEE Radar Conference
Abbreviated titleRadarConf22
Country/TerritoryUnited States
CityNew York City

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project

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.


  • Cognitive radar
  • probability calibration
  • resource management
  • target classification


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