Research on indoor fusion positioning algorithm based on 5G/UWB

This article proposes an indoor 5G and UWB fusion positioning algorithm to address the issues of a single positioning source being easily affected by environmental factors, low positioning accuracy, and poor robustness in indoor environments. This algorithm filters and aligns the data of 5G and UWB positioning units, and sends them to a BP neural network optimized by LM for training, outputting the positioning results. And conduct static and dynamic experiments at a driving school in Zengcheng District, Guangzhou. The experimental results show that under static testing, the LM-BP algorithm has a significant effect on accuracy improvement, with a 32.84% increase compared to the 5G ratio and a 24.24% increase compared to the UWB ratio. Under dynamic testing, the LM-BP algorithm has an 30.81% increase compared to the 5G ratio and an 18.89% increase compared to the UWB ratio.