LoT: A Transformer-Based Approach Based on Channel State Information for Indoor Localization

Fingerprint-based localization has gained significant commercial value in recent years and have brought new opportunities to location-based services in indoor environments. Existing fingerprint indoor positioning studies have focused on various neural networks (e.g., convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid approaches). However, these methods are limited by their inherent defects, such as local trapping in CNNs and gradient extinction in RNNs, which result in low localization accuracy and high training overhead. In this article, we devise a deep learning localization method, called localization transformer (LoT), to further enhance the indoor localization performance. Then, to address the problem of information loss caused by the input of the channel state information (CSI) matrix into LoT, we propose a trend-cyclical decomposition padding method that performs a trend-cyclical decomposition of the raw data to pad the dimensions. In addition, a rectangular patching strategy is designed to "patch" any shape of CSI matrix, so that the CSI matrix can be fed into a self-attention based learning module. Finally, by using a fully connected network (FCNN) to map the learned features, the 3-D coordinates can be estimated accurately. Experiments were conducted in indoor and urban canyon scenarios with three signal-to-noise ratios (SNRs). The experimental results indicate the mean absolute errors of LoT in indoor and urban scenes is 0.18 and 0.48 m, respectively, which shows that it outperforms the CSI fingerprint-based methods regarding localization accuracy.