Beyond Convolutions: Transformer Networks for Improved UWB CIR-based Fingerprinting

Indoor positioning using UWB has gained popularity due to its low cost while still providing centimeter-level accuracy. Currently, Convolutional Neural Network (CNN)-based approaches are often proposed for NLOS detection, error correction, etc. to make these UWB positioning systems more accurate. Transformer (TF) networks have shown to be a more capable alternative in several other domains, but have not been used for UWB fingerprinting. We present two novel TF-based approaches: one that processes channel impulse responses (CIR) directly, and a second one that uses cross-attention to incorporate anchor position information to improve geometric understanding. Moreover, we propose a second innovation by combining fingerprinting with a time calibration method that synchronizes the CIR data using a TDOA-based setup. This second innovation can be used with our novel approach, or with other state-of-the-art fingerprinting methods. The proposed models are evaluated in an industrial environment and outperform previous state-of-the-art CNNs in both LOS and NLOS situations, reaching accuracies with errors as low as 3 cm in real-life conditions, while having lower complexity and requiring fewer samples.