Single-Site Hybrid Positioning System Based on LOS Recognition

The indoor environment is much more complicated than the outdoor environment, which brings huge challenges to localization. In order to improve the accuracy and reliability of the indoor positioning system, we propose a single-site hybrid positioning (HP) system based on LOS recognition in this paper. The system adopts different positioning methods for different situations by distinguishing whether there is a line-of-sight (LOS) path in the environment. In this system, a LOS recognition algorithm is firstly designed to judge the LOS path depending on the received channel state information (CSI). According to the LOS recognition result, the HP system adaptively selects the optional positioning algorithm. When there is a LOS path, the positioning method based on parameter estimation is applied for localization. When there is no LOS path, a fingerprint-based positioning algorithm is proposed. Mean filtering and priciple component analysis (PCA) are used to do data dimensionality reduction and a combination of neural network (NN) and weighted k-nearest neighbour (WKNN) is used to complete position estimation. Finally the HP system is compared with other positioning systems, and the experimental results show that our proposed HP system can achieve higher positioning accuracy.