Abstract
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.
Highlights
The main contributions of this paper are as follows: (i) This paper proposes to use depthwise separable convolutions in the network to speed up network training and reduce localization delay in an indoor localization system based on a channel state information (CSI) fingerprint (ii) The proposed indoor localization system uses CSI feature images constructed from the amplitude difference of CSI subcarriers as location fingerprints
This paper proposed a passive CSI indoor localization system depthwise separable convolution-based passive indoor localization system (DSCP) by Wi-Fi wireless sensing
A neural network based on depthwise separable convolutions in fingerprint-based localization was adopted to speed up network training and reduce localization delay
Summary
Wang et al proposed deep learning-based CSI fingerprint indoor localization systems DeepFi and PhaseFi [23, 24] using the amplitude and phase of CSI, respectively. Most CSI fingerprint localization systems based on traditional methods have shortcomings, such as the single features of location fingerprint information and complex models. In the online localization phase, CSI feature images of test data are sent to the trained network to obtain the predicted location. (i) This paper proposes to use depthwise separable convolutions in the network to speed up network training and reduce localization delay in an indoor localization system based on a CSI fingerprint (ii) The proposed indoor localization system uses CSI feature images constructed from the amplitude difference of CSI subcarriers as location fingerprints.
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