Bayesian Optimization for Parameter Selection in Fusion Systems

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung

Abstract

In this paper, we propose a methodology for the application of Bayesian Optimization to the optimization of parameters in multi-sensor fusion systems. We apply this methodology to a state-of-the-art fusion model and demonstrate its efficacy in the optimization of fusion model parameters, including temporal decay, sensor priors and the event threshold, by employing Tree-Structured Parzen Estimators. The efficacy of the proposed methodology is evaluated by comparing the performance of the optimized system with that of a standard fusion system on a data set in the context of railway security. The results demonstrate a significant improvement in key metrics such as accuracy, false positive rate and F1-Score.
OriginalspracheEnglisch
TitelProceedings of the 2025 28th International Conference on Information Fusion, FUSION 2025
Seitenumfang7
ISBN (elektronisch)978-1-0370-5623-9
DOIs
PublikationsstatusVeröffentlicht - 26 Aug. 2025
Veranstaltung2025 28th International Conference on Information Fusion (FUSION) - Rio de Janeiro, Rio de Janeiro, Brasilien
Dauer: 7 Juli 202511 Juli 2025

Konferenz

Konferenz2025 28th International Conference on Information Fusion (FUSION)
Land/GebietBrasilien
StadtRio de Janeiro
Zeitraum7/07/2511/07/25

Research Field

  • Responsive Sensing & Analytics

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