As an important positioning source of indoor positioning technology, Wi-Fi signals have attracted the attention of researchers for a long time. Fingerprint positioning can solve the problems caused by non-line-of-sight propagation and multipath effects. To improve the accuracy of Wi-Fi indoor positioning, this study proposes an indoor positioning algorithm based on fine-grained channel state information (CSI) and convolutional neural network (CNN). CSI is a kind of observable measurement that better describes the nature of Wi-Fi signal propagation than received signal strength indication. This method uses the subcarrier amplitude and phase difference information extracted from CSI data to establish fingerprints. The clustering method is used to analyse the number of clusters of fingerprint data, and the fingerprint database is divided into two sub-databases according to the threshold. CNNs with the same network structure are used to train the two kinds of fingerprint sub-databases. In the positioning stage, the sub-database to which the data to be measured belongs is determined according to the calibration algorithm, and the corresponding CNN model is used to estimate the position. Experiments are performed in a typical indoor environment. Compared with existing fingerprint-based positioning methods, this method has higher positioning accuracy.

中文翻译：

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利用卷积神经网络对室内定位进行细粒度CSI指纹识别

作为室内定位技术的重要定位源，Wi-Fi信号已经引起了研究人员的长期关注。指纹定位可以解决由非视线传播和多径效应引起的问题。为了提高Wi-Fi室内定位的准确性，本研究提出了一种基于细粒度信道状态信息（CSI）和卷积神经网络（CNN）的室内定位算法。CSI是一种可观察的度量，它比接收信号强度指示更好地描述了Wi-Fi信号传播的性质。该方法使用从CSI数据中提取的子载波幅度和相位差信息来建立指纹。聚类方法用于分析指纹数据的聚类数量，指纹数据库根据阈值分为两个子数据库。具有相同网络结构的CNN用于训练两种指纹子数据库。在定位阶段，根据校准算法确定待测数据所属的子数据库，并使用相应的CNN模型估计位置。实验是在典型的室内环境中进行的。与现有的基于指纹的定位方法相比，该方法具有更高的定位精度。然后使用相应的CNN模型估算位置。实验是在典型的室内环境中进行的。与现有的基于指纹的定位方法相比，该方法具有更高的定位精度。然后使用相应的CNN模型估算位置。实验是在典型的室内环境中进行的。与现有的基于指纹的定位方法相比，该方法具有更高的定位精度。