A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN

Yaning Li, Hongsheng Li, Baoguo Yu and Jun Li
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Yaning Li: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Hongsheng Li: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Baoguo Yu: State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China
Jun Li: State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China

Future Internet, 2022, vol. 14, issue 8, 1-18

Abstract: At present, the interaction mechanism between the complex indoor environment and pseudolite signals has not been fundamentally resolved, and the stability, continuity, and accuracy of indoor positioning are still technical bottlenecks. In view of the shortcomings of the existing indoor fingerprint positioning methods, this paper proposes a hybrid CSI fingerprint method for indoor pseudolite positioning based on Ray Tracing and artificial neural network (RT-ANN), which combines the advantages of actual acquisition, deterministic simulation, and artificial neural network, and adds the simulation CSI feature parameters generated by modeling and simulation to the input of the neural network, extending the sample features of the neural network input dataset. Taking an airport environment as an example, it is proved that the hybrid method can improve the positioning accuracy in the area where the fingerprints have been collected, the positioning error is reduced by 54.7% compared with the traditional fingerprint positioning method. It is also proved that preliminary positioning can be completed in the area without fingerprint collection.

Keywords: CSI fingerprint; ANN; pseudolite; ray tracing; indoor positioning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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