An Enhanced WIFI Indoor Positioning Method Based on SNGAN

With the development of wireless communication technology research, location-based services (LBS) have emerged due to provide useful information to users based on the current user locations or a given location-indoor positioning technology has become a hot spot for research. Indoor positioning technology based on WIFI signals attentions owing to WIFI networks cover various indoor environments. Received signal strength (RSS) based indoor positioning technology has become an effective indoor positioning technology as a method that utilizes the wireless signal propagation model. However, due to the complexity and variability of the indoor environment, RSS values are highly vulnerable to noise and multipath interference, RSS-based WIFI fingerprinting can effectively avoid this problem. During the construction of RSS fingerprint maps, fingerprint data collection is time-consuming and laborious, how to use a small amount of fingerprint data to achieve high-precision positioning becomes a difficult point for fingerprint positioning technology. To solve this problem, a high-precision indoor localization method based on spectral normalization for generative adversarial networks (SNGAN) is proposed. The generative adversarial network with spectral normalization not only retains the original information to the greatest extent during the training process, but also makes the generated data closer to the distribution of real data and speeds up the training. Collecting a small amount of fingerprint data achieves higher localization accuracy.