GNSS Multipath Detection Aided by Unsupervised Domain Adaptation

Remy Zawislak, Marcus Greiff, Kyeong Jin Kim, Karl Berntorp, Stefano Di Cairano, Mao Konishi, Kieran Parsons, Philip V. Orlik, and Yuki Sato

Peer Reviewed

Abstract:	This paper concerns the problem of multipath detection in global navigation satellite system (GNSS) positioning. An unsupervised machine learning (ML) approach is developed using a convolutional neural network in an autoencoder framework with k-means clustering in the latent space. Such methods often rely on large amounts of annotated data during training, which are difficult to collect in the context of GNSS applications. To this end, a joint approach is proposed to train the ML method on synthetic data generated by hardware in the loop (HIL) simulations, and use unsupervised domain adaptation (UDA) to reduce any discrepancies between real and simulated data. For this purpose, an UDA facilitated by a cycle-consistent generative adversarial network is proposed. It is shown that the proposed method can significantly improve the multipath detection accuracy on experimental data compared to baseline approaches (including both heuristic approaches and ML-methods). It is also shown that UDA can enhance GNSS estimation performance in the absence of annotated training data, achieving a prediction accuracy in hand-labeled experimental data of as much as 99 %.
Published in:	Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) September 19 - 23, 2022 Hyatt Regency Denver Denver, Colorado
Pages:	2127 - 2137
Cite this article:	Zawislak, Remy, Greiff, Marcus, Kim, Kyeong Jin, Berntorp, Karl, Di Cairano, Stefano, Konishi, Mao, Parsons, Kieran, Orlik, Philip V., Sato, Yuki, "GNSS Multipath Detection Aided by Unsupervised Domain Adaptation," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 2127-2137. https://doi.org/10.33012/2022.18333
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