A Deep-Learning-Based Self-Calibration Time-Reversal Fingerprinting Localization Approach on Wi-Fi Platform

With the increasing demands for indoor location-based services, fingerprinting-based localization attracts considerable attention, due to that it could achieve high localization accuracy using simple equipment. However, the main problem of fingerprinting localization is that with the change of the indoor environment, the fingerprint database would be outdated, which inevitably leads to localization performance degradation. To tackle this issue, based on deep learning, this article proposes a self-calibration time-reversal (TR) fingerprinting localization approach to mitigate the effects of environmental changes without updating the fingerprint database. In the offline stage, the amplitude autoencoder (A-AE) and the phase autoencoder (P-AE) are, respectively, trained without labels to record features of the current environment. In the online stage, the trained A-AE and P-AE are used to adaptively calibrate the real-time measurements which may have been distorted due to the environmental changes. Based on the calibrated measurements, a modified TR resonating strength (TRRS) is presented for localization. The experimental results confirm the effectiveness of the proposal.

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