Robust indoor localization based on multi-modal information fusion and multi-scale sequential feature extraction

Magnetic-assisted indoor localization has attracted significant attention because of its commercial and social values. However, it is challenging to construct a robust and accurate system due to the severe feature ambiguity caused by different users, mobiles, attitudes, and moving speeds. In order to cope with this issue, we first propose to fuse magnetic with the multi-modal features, including Bluetooth low energy (BLE), and context information of continuous predictions, to improve the feature discrimination with more valuable localization clues. Then, we extract more orientation-insensitive magnetic features and remove the Direct Current (DC) component of the sequence can reduce the feature ambiguity caused by different holding attitudes, devices, and users. After that, we propose an online data augmentation algorithm to automatically generate a sufficient amount of various-speed sequences based on only one dense sampling benchmark sequence, thus reducing the influence of multi-scale sequences caused by different moving speeds. Finally, we propose a multi-branch and attention mechanism-based end-to-end localization model to extract and efficiently fuse the significant features of the multi-modal data for accurate localization. We evaluate the performance of the proposed localization system (DamLoc) in a typical indoor environment based on extensive experiments. Evaluation results showcase that DamLoc more robust for diverse heterogeneous factors, and can support about 63% improvement compared to state -of -the -art methods. It is worth pointing out that the BLE in our work can be replaced with other signals, such as Wireless Fidelity (Wi-Fi), which is more general than other fusion-based localization.