Abstract: In order to study the application of indoor positioning technology in complex environments, using stairs and laboratories as experimental scenarios, an indoor positioning method based on channel state information (CSI) and SVM regression is proposed. The method removes signal noise by density-based spatial clustering (DBSCAN) and extracts the fingerprint features that contribute the most using principal component analysis (PCA), while reducing the CSI fingerprint dimension. The SVM regression is used to establish a non-linear relationship between the CSI fingerprint and the target position, so as to achieve the purpose of estimating the target position based on the measured CSI fingerprint. The experimental results show that the positioning system can achieve a positioning accuracy of 1 m with a probability of more than 90% in the complex environment of staircases with strong multipath effects, and a positioning accuracy of 0.8 m with a probability of 82% in a laboratory environment. It shows that the indoor positioning method based on CSI and SVM regression has high efficiency and feasibility.

Key words: indoor positioning technology, channel state information, DBSCAN algorithm, principal component analysis, SVM regression