Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks

Aleksei Kuvshinov, Daniel Knobloch, Daniel Külzer, Elen Vardanyan, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

We consider the car key localization task using ultra-wideband (UWB) signal measurements. Given labeled data for a certain car, we train a deep classifier to make the prediction about the new points. However, due to the differences in car models and possible environmental effects that might alter the signal propagation, data collection requires considerable effort for each car. In particular, we consider a situation where the data for the new car is collected only in one environment, so we have to utilize the measurements in other environments from a different car. We propose a framework based on generative adversarial networks (GANs) to generate missing parts of the data and train the classifier on it, mitigating the necessity to collect the real data. We show that the model trained on the synthetic data performs better than the baseline trained on the collected measurements only. Furthermore, our model closes the gap to the level of performance achieved when we would have the information about the new car in multiple environments by 35 %.

OriginalspracheEnglisch
TitelIAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
Herausgeber (Verlag)Association for the Advancement of Artificial Intelligence
Seiten12552-12558
Seitenumfang7
ISBN (elektronisch)1577358767, 9781577358763
DOIs
PublikationsstatusVeröffentlicht - 30 Juni 2022
Veranstaltung36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Dauer: 22 Feb. 20221 März 2022

Publikationsreihe

NameProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Band36

Konferenz

Konferenz36th AAAI Conference on Artificial Intelligence, AAAI 2022
OrtVirtual, Online
Zeitraum22/02/221/03/22

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