Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability

Conference paper (2022)

Authors

P.O. Svenningsson Microwave Sensing, Signals & Systems - EEMCS
N.C. Kruse Microwave Sensing, Signals & Systems - EEMCS
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2022 P.O. Svenningsson, N.C. Kruse, F. Fioranelli, Alexander Yarovoy
DOI: https://doi.org/10.1109/RadarConf2248738.2022.9764292
Resource management Cognitive radar Probability calibration Target classification
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

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.