Enhanced UWB Ranging Utilizing Denoising Neural Network
Daeho Kim, Jae-Young Pyun
Abstract
This letter introduces a denoising-based two-way ranging (D-TWR) utilizing artificial intelligence (AI) for an ultra-wideband (UWB) indoor positioning system. The proposed D-TWR reduces the distance measurement error with a denoising neural network pre-trained on UWB channels in various indoor environments. The denoising model adopts an extended input layer comprising the TWR-measured distance and channel impulse response observations and infers the ground truth distance for UWB ranging. The experimental results provide insights into the performance of the proposed denoising AI model in various indoor environments. Furthermore, it was confirmed that our method can reduce the prediction error of the ranging distance by approximately 34.44% compared to the existing AI regression method, resulting in improved UWB positioning accuracy.