This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of each modality in the context of recurrent neural networks while accounting for the modality uncertainty which is directly learned from its own immediate past states. The proposed method was evaluated on the large-scale SPAWC2021 multi-modal localization dataset and compared with a wide range of baseline methods including the trilateration method, traditional fingerprinting methods, and convolution network-based methods.
@article{arxiv.2203.00510,
title = {Multi-Modal Recurrent Fusion for Indoor Localization},
author = {Jianyuan Yu and Pu and Wang and Toshiaki Koike-Akino and Philip V. Orlik},
journal= {arXiv preprint arXiv:2203.00510},
year = {2022}
}