PosFormer: Red generalizable de posicionamiento en interiores mediante fusión de características globales y locales

Sus antecedentes:

• Ultra-wideband (UWB) technology offers high-accuracy indoor positioning but struggles in multipath Non-Line-of-Sight (NLOS) environments.
• Existing methods, including geometry-based and deep learning approaches, face limitations in accurately capturing UWB signal propagation characteristics.
• Challenges include distance overestimation in traditional methods and insufficient feature extraction in current deep learning models.

Conclusiones:

• The proposed PosFormer model effectively addresses UWB indoor positioning challenges in NLOS environments.
• The dual-fusion network architecture and TL framework enhance accuracy, robustness, and data efficiency.
• PosFormer shows significant practical value for real-world UWB localization applications, especially in industrial settings.