Abstract
Indoor localization technology plays an important role in many indoor application scenarios. Existing WiFi-based indoor localization methods mainly obtain channel state information (CSI) through the personal computer, or obtain coarse-grained received signal strength (RSS) through the smartphone to finish the localization. Little work has been done on using smartphones to obtain fine-grained channel state information for localization. In this paper, we use the smartphone to collect fine-grained CSI that is more convenient and applicable, and propose a indoor fingerprinting localization. Compared with the CSI collected by the computer, the CSI signal collected by the smartphone fluctuates greatly. Hence, we corrects the CSI data through the signal processing technique and selects optimal subcarriers to obtain more stable and effective signals. In order to cope with the noisy WiFi environment, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is used to remove abnormal sample points to reduce environmental interference. Moreover, the support vector machine multi-classification method is used for training and classification to achieve localization. Finally, we use the Google Nexus 5 smartphone to conduct experiments in two typical indoor environments. The localization accuracy is 91% and 86%, respectively, and both average localization errors are less than 0.5m. Experimental results show that the proposed algorithm has higher localization accuracy compared with the typical algorithms.
Highlights
With the continuous development of wireless network technology [1], [2] and the rapid increase of mobile devices [3], [4], indoor localization and related applications have received extensive attention
Compared with the channel state information (CSI) collected by the computer, the CSI signal collected by the smartphone fluctuates greatly and the signal stability is poor, which is an inherent problem of CSI signal collected by smartphone
CSI SIGNAL PROCESSING Compared with the CSI collected by the computer, the CSI signal collected by the smartphone fluctuates greatly, which is an inherent problem of the CSI collected by the smartphone
Summary
With the continuous development of wireless network technology [1], [2] and the rapid increase of mobile devices [3], [4], indoor localization and related applications have received extensive attention. With the continuous development of network technology [16], channel state information (CSI) can be obtained from some WiFi network interface cards (NICs), which is more fine-grained. It uses subcarriers [17], [18] based on Orthogonal Frequency Division Multiplexing (OFDM) technology to obtain more abundant multipath information. This paper uses the smartphone instead of the computer to collect CSI for indoor localization for the first time, which is more convenient and applicable. In order to solve this challenge, this paper proposes the smartphone-based indoor fingerprinting localization using channel state information. Where |Hi| and Hi are the amplitude and phase of the i-th subcarrier, respectively
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