The Ultra-Wideband (UWB) Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution. However, the None-Line-of-Sight (NLOS) propagation may reduce the ranging accuracy for UWB localization system in indoor environment. So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy. In this paper, a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed. The proposed FCN-Attention algorithm utilizes a Fully Convolution Network (FCN) for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy. The proposed algorithm is evaluated using an open-source dataset, a local collected dataset and a mixed dataset created from these two datasets. The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24% on the open-source dataset, 100% on the local collected dataset and 92.01% on the mixed dataset, which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper.

Ultra Wideband (UWB); None-line-of-sight (NLOS) identification; channel impulse response (CIR); deep learning; fully convolution network; self-attention mechanism

Wuhan University