Device-independent cellular-based indoor location tracking using deep learning

Abstract The demand for a ubiquitous and accurate indoor localization service is continuously growing. Cellular-based systems are a good candidate to provide such ubiquitous service due to their wide availability worldwide. One of the main barriers to the accuracy of such services is the large number of models of cell phones, which results in variations of the measured received signal strength (RSS), even at the same location and time. In this paper, we propose OmniCells++, a deep learning-based system that leverages cellular measurements from one or more training devices to provide consistent performance across unseen tracking phones. Specifically, OmniCells++ uses a novel approach to multi-task learning based on LSTM encoder–decoder models that allows it to learn a rich and device-invariant RSS representation without any assumptions about the source or target devices. OmniCells++ also incorporates different modules to boost the system’s accuracy with RSS relative difference-based features and improve the deep model’s generalization and robustness. Evaluation of OmniCells++ in two realistic testbeds using different Android phones with different form factors and cellular radio hardware shows that OmniCells++ can achieve a consistent median localization accuracy when tested on different phones. This is better than the state-of-the-art indoor cellular-based systems by at least 148%.