GAN-Loc: Empowering Indoor Localization for Unknown Areas via Generative Fingerprint Map

As indoor localization becomes a necessity to provide intelligent location-based services for in-building users, fingerprint-based positioning has been widely adopted in numerous Wi-Fi-equipped devices. However, its reliance on extensive offline site surveys limits its application in unexplored environments without prior fingerprint sampling. To address the challenge, we propose GAN-Loc, a novel generative fingerprint-based frame-work. We extract the underlying correlation between location and radio signal features, empowering indoor localization in un/under-explored areas, including unknown data points, newly deployed APs, or unexplored sites. It involves: 1) decomposing into a signal feature map for each AP perspective; 2) learning with a set of points and their associated signal strength data; 3) generating and integrating synthetic radio fingerprints; and 4) employing them into some existing localization algorithms. We evaluated GAN-Loc with extensive real-world RSSI measurements in seven different indoor places. GAN-Loc achieves the localization accuracy of up to 1.21m, 1.29m, and 1.28m for Wi-Fi, Zigbee, and BLE, respectively, compared to the accuracy of 1.47m, 1.58m, and 1.22m using an ideal ground-truth map, which is unachievable without site survey in unknown sites.