Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM
This addresses indoor positioning for mobile users, but it appears incremental as it builds on existing deep learning methods.
The paper tackled indoor localization using smartphone magnetic data by proposing a multi-scale TCN and LSTM approach, achieving effective results as demonstrated in experiments.
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.