AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint
Construction in Indoor Localization System
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by
Qiyue Li, Heng Qu, Zhi Liu, Nana Zhou, Wei Sun, Jie Li
2018
Abstract
Wi-Fi positioning is currently the mainstream indoor positioning method, and
the construction of fingerprint database is crucial to Wi-Fi based localization
system. However, the accuracy requirement needs to sample enough data at many
reference points, which consumes significant manpower and time. In this paper,
we convert the CSI data collected at reference points into amplitude feature
maps and then extend the fingerprint database using the proposed Amplitude
Feature Deep Convolutional Generative Adversarial Network (AF-DCGAN) model.
Using this model, the convergence process in the training phase can be
accelerated, and the diversity of the CSI amplitude feature maps can be
increased significantly. Based on the extended fingerprint database, the
accuracy of indoor localization system can be improved with reduced human
effort.
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