Intelligent Indoor Localization Algorithm Based on Channel State Information

Author:

Chen Chao12,Wu Zhaoli23,Wang Xin24

Affiliation:

1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, P. R. China

2. School of Information and Electronics Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221000, P. R. China

3. Xuzhou Intelligent Machine Vision Engineering and Technology, Center, Xuzhou 221000, P. R. China

4. Jiangsu Collaborative Innovation Center for Building, Energy Saving and Construct Technology, Xuzhou 221000, P. R. China

Abstract

With the deepening application of wearable devices in the field of indoor positioning, the mining and application of human behavior patterns based on new technologies such as mobile terminals, channel technologies and intelligent algorithms have gradually become a research hotspot in the field of intelligent positioning. At this stage, outdoor positioning satellite technology has been gradually improved, but it cannot work effectively in indoor environment, and indoor positioning technology has not formed a set of standard and effective scheme at this stage, which leads to a scene of a hundred contentions for indoor positioning technology. This paper proposes an indoor intelligent localization algorithm based on channel state information, which first forms the measurements of antenna subarrays by the fixed antenna subset arrays located in different paths to achieve the phase estimation based on channel state; then determines the AoA of the direct path from the target to the AP using ToF information and eliminates the STO noise of channel state information by unfolding the best linear fit of CSI phase; again the AoA and ToF estimates from multiple measurements are plotted in two-dimensional space, and the direct path possibility estimation is achieved by identifying the estimates through five clusters of Gaussian-averaged clustering algorithm; finally the coordinates of the target to be located are obtained by coupling direct propagation path, estimation clustering and least-squares estimation using three APs as the receiver and the target to be located as the transmitter. The simulation results show that the intelligent localization algorithm proposed in this paper is able to lack the localization accuracy at the decimeter level in the indoor localization environment with three fixed APs, and the average error is better than the localization algorithms of support vector machine, deep learning and limit learning.

Funder

National Natural Science Foundation of China

Key University Science Research Project of Jiangsu Province

Jiangsu Collaborative Innovation Center for Building Energy Saving and Construct Technology Project

Xuzhou policy guidance program

Key Technology Research and Development Program of Shandong

Jiangsu Construction System Science and Technology Project

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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