Redpin - adaptive, zero-configuration indoor localization through user collaboration
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
RF-Based Initialisation for Inertial Pedestrian Tracking
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
WASP: an enhanced indoor locationing algorithm for a congested Wi-Fi environment
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
Smartphone-Based Collaborative and Autonomous Radio Fingerprinting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Indoor positioning is one of the key components enabling retail-related services such as location-based product recommendations or in-store navigation. In the recent years, active research has shown that indoor positioning systems based on Wi-Fi fingerprints can achieve a high positioning accuracy. However, the main barrier of broad adoption is the labor-intensive process of collecting labeled fingerprints. In this work, we propose an approach for reducing the amount of labeled data instances required for training a Wi-Fi fingerprint model. The reduction of the labeling effort is achieved by leveraging dead reckoning and an active learning-based approach for selecting data instances for labeling. We demonstrate through experiments that we can construct a Wi-Fi fingerprint database with significantly less labels while achieving a high positioning accuracy.