Improved GNSS Cooperation Positioning Algorithm for Indoor Localization
Taoyun Zhou1, 2, Baowang Lian1, Siqing Yang2, *, Yi Zhang1, Yangyang Liu1, 3
1 School of Electronics and Information, Northwestern Polytechnical University, Shaanxi, 710072, China.
2 School of Information, Hunan University of Humanities, Science and Technology, Hunan, 417000, China.
3 Radio Science Laboratory, University of British Columbia, Vancouver, Canada.
* Corresponding Author: Siqing Yang. Email: 
.
Computers, Materials & Continua https://doi.org/10.3970/cmc.2018.02671
Abstract
For situations such as indoor and underground parking lots in which satellite signals are obstructed, GNSS cooperative positioning can be used to achieve high-precision positioning with the assistance of cooperative nodes. Here we study the cooperative positioning of two static nodes, node 1 is placed on the roof of the building and the satellite observation is ideal, node 2 is placed on the indoor windowsill where the occlusion situation is more serious, we mainly study how to locate node 2 with the assistance of node 1. Firstly, the two cooperative nodes are located with pseudo-range single point positioning, and the positioning performance of cooperative node is analyzed, therefore the information of pseudo-range and position of node 1 is obtained. Secondly, the distance between cooperative nodes is obtained by using the baseline method with double-difference carrier phase. Finally, the cooperative location algorithms are studied. The Extended Kalman Filtering (EKF), Unscented Kalman Filtering (UKF) and Particle Filtering (PF) are used to fuse the pseudo-range, ranging information and location information respectively. Due to the mutual influences among the cooperative nodes in cooperative positioning, the EKF, UKF and PF algorithms are improved by resetting the error covariance matrix of the cooperative nodes at each update time. Experimental results show that after being improved, the influence between the cooperative nodes becomes smaller, and the positioning performance of the nodes is better than before.
Keywords
Indoor localization, GNSS cooperative positioning, extended kalman filtering (EKF), unscented kalman filtering (UKF), particle filtering (PF)
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