Enhancing Indoor Localization Accuracy in Dense IoT-Integrated 5GNR Networks: Introducing SGNCL for Sensor-Guided NLoS Correction Localization

Abstract

In the rapidly advancing field of wireless localization, achieving accurate indoor tracking is crucial for the next generation of smart factories, automated workflows, and efficient supply chains. The integration of 5G networks within industrial environments offers high connectivity, yet challenges remain in obtaining the fine-grained positioning required for localization applications. This article presents the development and simulation-based evaluation of the sensor-guided non-line-of-sight (NLoS) corrective localization (SGNCL) algorithm within the 5G New Radio network framework. The proposed algorithm utilizes data integration techniques to effectively mitigate NLoS errors, which are prevalent in complex indoor environments with high delay spreads. We describe the algorithm's design, operational principles, and the comprehensive simulation setup used to assess its performance. In comparison to the minimum variance anchor set, which exhibited a mean error of 2.5 m, the SGNCL algorithm achieved a significant improvement, reducing the mean error to 0.86 m. The results also highlight the algorithm's ability to handle varying delay spreads and sensor densities, ensuring robust localization performance across different scenarios. These findings demonstrate the potential of the SGNCL algorithm to enhance 5G-enabled indoor localization services by addressing NLoS challenges through simulation-based insights.