Research on ultra wide band NLoS/LoS recognition method based on the fusion of 1DCNN and LSTM

A deep learning model based on one-dimensional-convolutional neural network-convolutional long short-term memory (LSTM) attention network (1DCNN-CLANet) was proposed to improve the ranging accuracy and positioning performance of ultra wide band (UWB) localization systems under non-line-of-sight (NLoS) conditions. Convolutional neural network (CNN) was first employed to extract spatial features from channel impulse response (CIR) data, and LSTM network was used to capture their temporal characteristics. Then, CNN was further applied to extract additional features such as distance data, signal amplitude, and maximum noise strength. Finally, An attention mechanism was then incorporated to fuse the CIR and additional feature branches for accurate NLoS/LoS classification. Experimental results show that 1DCNN-CLANet achieves classification accuracies of 99.51% for binary classification and 98.47% for four-class classification in complex environments, outperforming other approaches. The model demonstrates strong potential for robust NLoS identification in practical UWB localization systems.