TY - GEN
T1 - Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks
AU - Kuvshinov, Aleksei
AU - Knobloch, Daniel
AU - Külzer, Daniel
AU - Vardanyan, Elen
AU - Günnemann, Stephan
N1 - Publisher Copyright:
Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2022/6/30
Y1 - 2022/6/30
N2 - 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 %.
AB - 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 %.
UR - http://www.scopus.com/inward/record.url?scp=85147606457&partnerID=8YFLogxK
U2 - 10.1609/aaai.v36i11.21526
DO - 10.1609/aaai.v36i11.21526
M3 - Conference contribution
AN - SCOPUS:85147606457
T3 - Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
SP - 12552
EP - 12558
BT - IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
PB - Association for the Advancement of Artificial Intelligence
T2 - 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Y2 - 22 February 2022 through 1 March 2022
ER -