Counteracting Packet Loss in Fingerprint-Based Indoor Positioning via Spatially Regularized Entropy and Ground-Truth Prior Variational Inference

Indoor positioning system (IPS) enables real-time tracking and positioning within indoor environments and supports various location-based services (LBSs) across different application settings. The growth of the Internet of Things (IoT) has enabled fingerprint-based IPS to achieve sub-meter or centimeter-level accuracy. However, packet loss issues caused by attenuation, interference from other devices, and noise hinder the fingerprint-based IPS. Despite developments in IPS methods, such as fingerprint augmentation and the integration of variational inference, effectively using these techniques with fingerprints under packet loss to achieve robust positioning continues to pose a challenge. This article develops a real-time positioning model named Packet Loss Indoor Positioning Net (PLIPNet). PLIPNet combines the variational inference process and encodes statistical means of fingerprints in each location as prior distributions of latent variables, making it free from prior parameter configurations. The location's spatial information is incorporated into latent space and probability representation to improve latent distribution distinguishability and avoid significant positioning errors. Comparisons with state-of-the-art positioning models show that PLIPNet consistently performs the best under various packet loss settings. For instance, when the packet loss rate reaches 80%, PLIPNet achieves a localization error of only 25.7% of that achieved by the best existing model.