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.

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WiFi positioning is currently the more mainstream indoor positioning method, and fingerprint database construction is crucial to WiFi-based localization systems. However, this approach requires enough fingerprint data for a single point. In this paper, we convert channel state information (CSI) data into amplitude feature maps to construct initial fingerprint library and then extend the fingerprint database using the proposed improved deep convolutional generative adversarial nets (IDCGAN) model. Finally, the amplitude feature maps are trained by the CNN to locate. Based on the extended fingerprint database, the accuracy of indoor localization systems can be improved with reduced human effort.

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