TITLE:
UWB Localization Algorithm Based on BiLSTM and Bahdanau Attention
AUTHORS:
Guanhui Li, Guoliang Wei, Zhuang Xi
KEYWORDS:
Ultra-Wide Band, Non-Line-of-Sight, Bidirectional Long Short-Term Memory Network, Bahdanau Attention Mechanism
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.5,
May
27,
2026
ABSTRACT: Ultra Wide Band (UWB) technology has a wide range of applications in indoor positioning due to its high precision and strong anti-interference ability. However, in complex indoor environments, UWB signals are susceptible to multipath effects and non line of sight conditions, leading to a decrease in positioning accuracy. A UWB high-precision positioning algorithm based on Bidirectional Long Short Term Memory (BiLSTM) network and Bahdanau attention is proposed to address this issue. By using BiLSTM network to deeply model the temporal dependence of TOA observation sequence, and embedding Bahdanau attention mechanism, adaptive weight allocation is implemented on the feature information of UWB signal to highlight the contribution of key features to the positioning results. The experimental results show that the proposed algorithm can effectively suppress the interference caused by NLOS, ultimately achieving an average positioning error of 6.7 cm. Compared with UWB positioning algorithms based on Recurrent Neural Network (RNN), LSTM, and Gated Recurrent Unit (GRU), the positioning errors are reduced by 40.71%, 37.15%, and 37.79%, respectively. The experimental results show that the deep learning model combining BiLSTM and Bahdanau attention mechanism has higher robustness and localization accuracy in complex environments.