A Low-Overhead Indoor Positioning System Using CSI Fingerprint Based on Transfer Learning
Yong Zhang, Chengbin Wu, Yang Chen
Abstract
With the rapidly growing demand for Location-Based Services in indoor environments, fingerprint-based indoor positioning has caused great interest due to its high positioning accuracy and low equipment cost. However, the standard signal radio map cannot provide consistent high positioning accuracy under environmental changes and new scenarios. To address this problem, we present a novel indoor positioning Transfer Learning(TL) system based on improved TrAdaBoost. First perform phase correction on the raw CSI(Channel State Information) phase, then use One-vs-Rest algorithm and One-Hot coding, which can realize the multi-classification ability of the TrAdaBoost algorithm. Meanwhile, optimize the iterative process of the algorithm by using a correction factor. In addition, Confidence regression is used to obtain the final estimated position. Experimental results show that the positioning accuracy can be improved by 35% in dynamic environment conditions. The proposed method can improve the positioning accuracy by an average of 30% in new scenes and the Site Survey Overhead(SSO) is reduced by 40%. Compared to other position b algorithm, our proposed method has more robustness in time and space.