Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM

TL;DR

This paper introduces a multi-scale TCN and LSTM framework for indoor magnetic localization, improving signal discernibility and addressing time-series inconsistencies to enhance localization accuracy.

Contribution

The paper proposes a novel multi-scale TCN and LSTM-based approach with multi-scale TCN layers to improve indoor magnetic localization accuracy.

Findings

Effective in indoor magnetic localization

Improves signal feature extraction

Addresses time-series speed variability

Abstract

A novel multi-scale temporal convolutional network (TCN) and long short-term memory network (LSTM) based magnetic localization approach is proposed. To enhance the discernibility of geomagnetic signals, the time-series preprocessing approach is constructed at first. Next, the TCN is invoked to expand the feature dimensions on the basis of keeping the time-series characteristics of LSTM model. Then, a multi-scale time-series layer is constructed with multiple TCNs of different dilation factors to address the problem of inconsistent time-series speed between localization model and mobile users. A stacking framework of multi-scale TCN and LSTM is eventually proposed for indoor magnetic localization. Experiment results demonstrate the effectiveness of the proposed algorithm in indoor localization.