Enhancing Indoor Localization With Semi-Crowdsourced Fingerprinting and GAN-Based Data Augmentation

The popularity of radio frequency (RF)-based fingerprinting for indoor localization has grown owing to its relatively low cost of equipment deployment and satisfactory accuracy. However, generating a complete radio map by associating unlabeled RF signals with the corresponding location information remains challenging, especially in crowdsourcing-based fingerprinting. In this article, we propose a semi-crowdsourced radio map construction method based on Bluetooth low-energy (BLE) landmarks that harnesses reference points (RPs) in the radio map for coarse localization and facilitates the labeling of location information to WiFi signals. Principally, we acquire RF-received signal strength (RSS) measurements annotating them with location coordinates recorded while a user is walking to provide an efficient method of data collection. Furthermore, we introduce a generative adversarial network (GAN)-based method to increase the amount of training data collected at each RP and reduce human effort by augmenting the fingerprint database. Our proposed method demonstrates promising results, including improved localization accuracy and localization performance comparable to that of traditional site surveys while reducing measurement time and human effort.