Enriching Indoor Localization Fingerprint using A Single AC-GAN

For WiFi indoor localization, collecting the data to construct the fingerprint dataset is a time-consuming and laborious job. In this paper, we propose to alleviate the time-intensive data-labeling by generating artificial fingerprint using a single AC-GAN. Compared to the existing multi-GAN approaches, we show that the use of a single AC-GAN could achieve the improved accuracy in localization, which is validated by extensive experiments. We also discuss its potential limit.