Calibration of Cognitive Classification Systems for Radar Networks for Increased Reliability
P.O. Svenningsson (TU Delft - Microwave Sensing, Signals & Systems)
N.C. Kruse (TU Delft - Microwave Sensing, Signals & Systems)
F. Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
Alexander Yarovoy (TU Delft - Microwave Sensing, Signals & Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.