In this article, we propose an automatic Wi-Fi fingerprint system that combines an unsupervised dual radio mapping (UDRM) algorithm with the aim of reducing the time-cost needed to acquire Wi-Fi signals. Our proposed system is appropriate for indoor environments and utilizes a minimum description length principle (MDLP)-based radio map feedback (RMF) algorithm that simultaneously optimizes and updates the radio map. In the training phase, the proposed UDRM algorithm generates a radio map of the entire building based on the measured radio map of one reference floor. It does this by selectively applying a modified autoencoder and a generative adversarial network according to the spatial structures. Our proposed learning-based UDRM algorithm does not require labeled data, which is essential for supervised and semisupervised learning algorithms. It has a relatively low dependence on received signal strength indicator (RSSI) data sets. Our proposed RMF algorithm analyzes the distribution characteristics of the RSSIs for newly measured access points (APs) and feeds the analyzed results back to the radio map. The MDLP, which is applied to the proposed algorithm, improves the positioning performance and optimizes the size of the radio map by preventing the indefinite updating of the RSSI and by updating the newly added APs in the radio map.

中文翻译：

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使用自动编码器和 GAN 的基于选择性无监督学习的 Wi-Fi 指纹系统


在本文中，我们提出了一种自动 Wi-Fi 指纹系统，该系统结合了无监督双无线电映射 (UDRM) 算法，旨在减少获取 Wi-Fi 信号所需的时间成本。我们提出的系统适用于室内环境，并利用基于最小描述长度原则 (MDLP) 的无线电地图反馈 (RMF) 算法，同时优化和更新无线电地图。在训练阶段，所提出的 UDRM 算法根据一个参考楼层的测量无线电地图生成整个建筑物的无线电地图。它通过根据空间结构有选择地应用修改后的自动编码器和生成对抗网络来实现这一点。我们提出的基于学习的 UDRM 算法不需要标记数据，这对于监督和半监督学习算法至关重要。它对接收信号强度指示器 (RSSI) 数据集的依赖性相对较低。我们提出的 RMF 算法分析新测量的接入点 (AP) 的 RSSI 分布特征，并将分析结果反馈到无线电地图。应用于该算法的MDLP通过防止RSSI的无限更新以及更新无线电地图中新添加的AP来提高定位性能并优化无线电地图的大小。