SWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking

Abstract

Indoor/outdoor localization topic has gained a significant research interest due to the wide range of potential applications. Commonly, the Fingerprinting methods for spatial characterization of the environments monitored are employed in deterministic/statistical estimation. However, there are Fingerprint parameters that are generally neglected and can seriously affect the performance yielding to low accurate location. Nowadays, machine and deep learning (DL) methods are employed in this topic due to its ability to approximate complex non-linear models being capable of mitigating the undesirable effects of wireless propagation. In this paper, a complete overview of most influential aspects in Fingerprinting and indoor tracking methods is presented. Furthermore, a novel multi-modal complete tracking system, called SWiBluX, based on statistic and DL techniques is presented. The system relies on relevant feature extraction from available data sources to estimate user's/target indoor position using a multi-phase statistical Fingerprint and DL disruptive approach. In addition, a Gaussian outlier filter is applied to the position estimation model output to further reduce the error in the estimation. The set of experiments performed shows that Fingerprint positioning accuracy estimation can be improved up to 45% resulting in a final estimation error that outperforms related literature.