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In this paper, the focus is on the localization of drones by using New Radio (NR) Fifth Generation (5G) downlink positioning reference signals (PRS), especially for outdoor Global Positioning System (GPS) limited environments. The system calculates the position through the hyperbolic multilateration method based on the time difference of arrival, and the observed time difference of arrival. In this study, a new approach is proposed that employs a particle filter algorithm to reduce estimation errors and improve positioning accuracy. To implement the model, a software-defined radio system with a Universal Software Radio Peripheral (USRP) is used. The system incorporates path loss models from 3GPP TR 38.901-based scenarios. The particle filtering-based technique improves the positioning estimation of drones.

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Drone Positioning Estimation with Particle Filtering Augmented NR Positioning Reference Signal

  • 会议论文

  • 发表日期：

    2026年

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  • N. Suba Renjani,
  • A. Rajesh,
  • C. Manikandan,
  • G. Suguna,
  • J. Saranya,
  • R. Selvakumar

摘要

In this paper, the focus is on the localization of drones by using New Radio (NR) Fifth Generation (5G) downlink positioning reference signals (PRS), especially for outdoor Global Positioning System (GPS) limited environments. The system calculates the position through the hyperbolic multilateration method based on the time difference of arrival, and the observed time difference of arrival. In this study, a new approach is proposed that employs a particle filter algorithm to reduce estimation errors and improve positioning accuracy. To implement the model, a software-defined radio system with a Universal Software Radio Peripheral (USRP) is used. The system incorporates path loss models from 3GPP TR 38.901-based scenarios. The particle filtering-based technique improves the positioning estimation of drones.