Factor Graph-Based Cooperative Positioning Algorithm for Pedestrian Navigation Systems in Indoor Environments

Chunyang Yu, Haiyu Lan, Yiran Luo, Shiwei Fan, Naser El-Sheimy

Abstract: This research presents a Factor Graph-based cooperative positioning algorithm for multiple pedestrian navigation systems (PNSs) in indoor global navigation satellite system (GNSS) denied environments. Smartphone embedded sensors, such as MEMS gyroscopes, MEMS accelerometers and magnetometer, and the distance between different PNS are integrated through Factor Graph-based algorithm. Different from the traditional methods (i.e. the Extended Kalman filter (EKF) and Unscented Kalman filter (UKF)). which have large linearity error under the condition of nonlinear observation equation, the proposed Factor Graph-based algorithm utilized the Baysis filter to solve the PNS cooperative problem. Factor graph and sum-product (FGSP) based cooperative positioning algorithm is established to mathematically implement the Bayse filter by converting the global function estimation problem into local function sum-product estimation problem. The proposed algorithm has important theoretical and practical value for both industry and academic areas.
Published in: Proceedings of the ION 2019 Pacific PNT Meeting
April 8 - 11, 2019
Hilton Waikiki Beach
Honolulu, Hawaii
Pages: 628 - 634
Cite this article: Yu, Chunyang, Lan, Haiyu, Luo, Yiran, Fan, Shiwei, El-Sheimy, Naser, "Factor Graph-Based Cooperative Positioning Algorithm for Pedestrian Navigation Systems in Indoor Environments," Proceedings of the ION 2019 Pacific PNT Meeting, Honolulu, Hawaii, April 2019, pp. 628-634.
https://doi.org/10.33012/2019.16828
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