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

Author

Listed:
  • Yaning Li

    (School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
    State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China
    The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, 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
    The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China)

  • Jun Li

    (State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China
    The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China)

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.

Suggested Citation

  • Yaning Li & Hongsheng Li & Baoguo Yu & Jun Li, 2022. "A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN," Future Internet, MDPI, vol. 14(8), pages 1-18, July.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:235-:d:875815
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    References listed on IDEAS

    as
    1. Guangwei Fan & Chuanzhen Sheng & Baoguo Yu & Lu Huang & Qiang Rong, 2022. "An Indoor and Outdoor Multi-Source Elastic Fusion Navigation and Positioning Algorithm Based on Particle Filters," Future Internet, MDPI, vol. 14(6), pages 1-16, May.
    2. Antonio Del Corte-Valiente & José Manuel Gómez-Pulido & Oscar Gutiérrez-Blanco & José Luis Castillo-Sequera, 2019. "Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios," Energies, MDPI, vol. 12(15), pages 1-23, July.
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