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<p>Precise user localization is a key capability of emerging Fifth Generation (5G) and beyond-5G networks, enabling advanced applications in intelligent sensing, vehicular navigation, and industrial automation. Conventionally, as per the 3rd Generation Partnership Project (3GPP), 5G New Radio (NR) supports several positioning techniques based on time-of-arrival (ToA), time-difference-of-arrival (TDoA), and angle-of-arrival (AoA) measurements. Time-based methods are limited by synchronization errors and signal reflections. Conversely, angle-based methods increase complexity and deployment costs because they require large antenna arrays and precise calibration. To overcome and enhance positional accuracy, incorporate an optimization algorithm by using Time Difference of Arrival (TDoA) measurements extracted from the Positioning Reference Signal (PRS), and then an initial estimate is obtained using the least squares technique. To improve positioning reliability in noisy or multipath-rich environments, this estimate is refined using optimization algorithms, Iteratively Reweighted Least Squares (IRLS), and Gauss–Newton (GN) algorithms. The approach incorporates 3GPP delay-spread-dependent bias modeling across indoor and outdoor scenarios. Results demonstrate that IRLS algorithm significantly lowers positioning error, with Root Mean Square Error (RMSE) values of 7.7and 15.5&#xa0;m in indoor office and 8.1 and 30.3&#xa0;m in outdoor urban scenarios for LoS and NLoS environments, respectively.</p>

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Enhanced TDoA based 5G NR positioning using Gauss–Newton and IRLS algorithms in LoS and NLoS environments

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Gousiya SK,
  • Sridhar Miriyala,
  • Bhavana Dokku,
  • Sai Krishna Santosh Gollapudi,
  • Venkata Ratnam Devanaboyina

摘要

Precise user localization is a key capability of emerging Fifth Generation (5G) and beyond-5G networks, enabling advanced applications in intelligent sensing, vehicular navigation, and industrial automation. Conventionally, as per the 3rd Generation Partnership Project (3GPP), 5G New Radio (NR) supports several positioning techniques based on time-of-arrival (ToA), time-difference-of-arrival (TDoA), and angle-of-arrival (AoA) measurements. Time-based methods are limited by synchronization errors and signal reflections. Conversely, angle-based methods increase complexity and deployment costs because they require large antenna arrays and precise calibration. To overcome and enhance positional accuracy, incorporate an optimization algorithm by using Time Difference of Arrival (TDoA) measurements extracted from the Positioning Reference Signal (PRS), and then an initial estimate is obtained using the least squares technique. To improve positioning reliability in noisy or multipath-rich environments, this estimate is refined using optimization algorithms, Iteratively Reweighted Least Squares (IRLS), and Gauss–Newton (GN) algorithms. The approach incorporates 3GPP delay-spread-dependent bias modeling across indoor and outdoor scenarios. Results demonstrate that IRLS algorithm significantly lowers positioning error, with Root Mean Square Error (RMSE) values of 7.7and 15.5 m in indoor office and 8.1 and 30.3 m in outdoor urban scenarios for LoS and NLoS environments, respectively.