A Learning-Based Sequence-to-Sequence WiFi Fingerprinting Framework for Accurate Pedestrian Indoor Localization Using Unconstrained RSSI

Indoor location-based services are essential to daily life but lack a standard like outdoor Global Positioning System. wireless fidelity (WiFi) received signal strength (RSS) is an optimal option thanks to its ubiquitous deployment, though existing research typically uses only a few constrained devices. We present a learning-based WiFi RSS indicator (RSSI) fingerprinting method designed for general environments. RSSI samples are collected in daily scenarios with hundreds of WiFi access points (APs) without disclosing their locations, and with randomly distributed reference points. The continuously measured RSSI values across multiple timestamps are treated as sequences, serving as fingerprints corresponding to location sequences. Instead of using all detected APs, we discard those sensed only in limited timestamps, i.e., APs with confined coverage and limited identification positions. We then employ various 1-D feature extractors to estimate the location sequence from the refined RSSI indices. Our method outperforms state-of-the-art methods on open-access datasets from a small office with densely equipped WiFi APs and a larger university campus with sparse WiFi signals. Real-world experiments on the CUHK campus further demonstrate the statistical consistency of the proposed method. We share the data collection code and self-collected data to facilitate future studies.