  With widely deployed WiFi network and the uniqueness feature (fingerprint) of
wireless channel information, fingerprinting based WiFi positioning is
currently the mainstream indoor positioning method, in which fingerprint
database construction is crucial. However, for accuracy, this approach requires
enough data to be sampled at many reference points, which consumes excessive
efforts and time. In this paper, we collect Channel State Information (CSI)
data at reference points by the method of device-free localization, then we
convert collected CSI data into amplitude feature maps and extend the
fingerprint database using the proposed Amplitude-Feature Deep Convolutional
Generative Adversarial Network (AF-DCGAN) model. The use of AF-DCGAN
accelerates convergence during the training phase, and substantially increases
the diversity of the CSI amplitude feature map. The extended fingerprint
database both reduces the human effort involved in fingerprint database
construction and the accuracy of an indoor localization system, as demonstrated
in the experiments.
