Multi-Modal Recurrent Fusion for Indoor Localization

TL;DR

This paper introduces a multi-modal recurrent fusion approach for indoor localization that combines Wi-Fi, IMU, and UWB signals using neural networks, improving accuracy by modeling modality uncertainty.

Contribution

It presents a novel multi-stream recurrent fusion method that effectively integrates multiple wireless signal modalities for indoor localization, accounting for modality uncertainty.

Findings

Outperforms baseline methods on SPAWC2021 dataset

Effectively models modality uncertainty in localization

Achieves higher accuracy than traditional techniques

Abstract

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.