Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

中文翻译：

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DSCP：使用CSI指纹的基于深度可分离卷积的被动室内定位

基于Wi-Fi的室内定位已在无线传感领域引起了广泛关注。但是，大多数基于Wi-Fi的室内定位系统具有复杂的模型和较高的定位延迟，这限制了这些定位方法的通用性。为了解决这些问题，提出了一种基于深度可分离卷积的被动室内定位系统（DSCP）。DSCP是一种基于指纹的轻量级本地化系统，包括离线培训阶段和在线本地化阶段。在离线训练阶段，首先将室内场景划分为不同区域，以设置用于收集CSI的训练位置。然后，提取这些CSI子载波的幅度差以构造位置指纹，从而训练卷积神经网络（CNN）。在在线本地化阶段，首先在测试位置收集CSI数据，然后提取位置指纹，最后将其输入到经过训练的网络中以获得预测位置。实验结果表明，DSCP训练时间短，定位延迟低。在典型的室内场景中，DSCP实现了较高的定位精度（高于97％），并且中位定位距离误差较小，仅为0.69 m。