  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.
