Fuzzy Transformer Machine Learning for UWB NLOS Identification and Ranging Mitigation

Abstract

Ultrawideband (UWB) is a high-precision positioning and navigation technology, it faces significant challenges due to the abundance of non-line-of-sight (NLOS) conditions in complex indoor environments. In this study, we introduce the bidirectional encoder representations from transformers (BERTs) to identify and mitigate the impact of NLOS paths using the channel impulse response (CIR). We derive three new CIR features that comprise both the time and energy characteristics of CIR sequences. These proposed features are fused with fuzzy probabilities into BERT (F-BERT), in order to identify the NLOS paths. Based on the NLOS identification results from F-BERT, a ranging classification and mitigation strategy with another BERT is further designed to enhance the ranging and positioning accuracy. The experimental results indicate that F-BERT outperforms state-of-the-art algorithms such as least-squares support vector machine (LS-SVM), convolutional neural network (CNN), and CNN with long short-term memory (CNN-LSTM) by 12.5%, 13.9%, and 14.9%, respectively, in terms of NLOS identification accuracy with LOS and NLOS recall. The proposed BERT also outperforms the existing algorithms by 36.2% in ranging error reduction in an NLOS environment. Furthermore, our proposed algorithms similarly outperform existing algorithms in mean positioning accuracy by 37.9%. Finally, our BERT algorithms achieve generality as, although they were trained in one environment, they are shown to still work well in another unknown environment.