Indoor Localization With Adaptive Signal Sequence Representations

Indoor location-based services (LBS) have exhibited large commercial and social values in smart cities, and urgent demands of which have spurred many localization techniques. Existing indoor localization approaches mostly rely on fingerprint techniques, leveraging either spatially discrete fingerprints or temporally consecutive ones for localization. However, these approaches often suffer from large errors or high time overhead in practice due to signal ambiguities or long input sequences. To overcome these drawbacks, this paper proposes a framework utilizing multiple adaptive representations of signal sequences for localization, where each representation indicates a corresponding signal structure with underlying location clues. As an example, the proposed approach takes geomagnetic signal sequences as input and infers location features from two intuitive representations, e.g., spatial and temporal ones. With adaptive signal representations, the proposed approach takes specifically optimized neural networks to extract corresponding location clues respectively and fuses them to generate more distinguishing features for more accurate localization. Furthermore, the ensemble learning mechanism is adopted in the approach and a weighted k-NN based location estimation algorithm is devised to enhance the robustness. Extensive experiments in three different trial sites demonstrate that the proposed approach outperforms state-of-the-art competing schemes by a wide margin, reducing mean localization error by more than 46%.