Domain-Adversarial Neural Network for UWB NLOS Identification in Multiple Environments.

Accurate recognition of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) signals is crucial for mitigating positioning errors and improving the positioning performance of Ultra-Wideband (UWB) localization systems. Current NLOS identification methods are limited to the specific measurement environments and fail to exhibit effective cross-domain adaptability, being unable to generalize to unseen environments. To address these challenges, we propose a novel NLOS identification strategy based on a Domain-Adversarial Neural Network (DANN). Firstly, aiming at the problem that traditional feature extraction methods fail to capture the deep nonlinear characteristics of Channel Impulse Response (CIR) data, we develop a CNN-DAE-MLP-Attention (CDM) hybrid model for high-quality channel feature extraction, which takes both raw CIR data and handcrafted channel features into account. Secondly, we integrate the CDM model into the DANN framework by replacing its original shallow feature extraction module to further propose the CDMD algorithm; by combining the robust feature representation capability of CDM with the excellent domain adaptation capability of DANN, the proposed CDMD algorithm achieves enhanced performance in cross-domain LOS/NLOS identification. Finally, the effectiveness of the proposed algorithm is verified using measured data from different scenarios. Results demonstrate that the proposed algorithm possesses strong generalization ability. For cross-domain NLOS recognition from underground parking garage to corridor and underground parking garage to lobby, the proposed method achieves accuracies of 77.00% and 72.84%, respectively. Moreover, the results indicate that only a limited number of target-domain samples are sufficient for the model to achieve accurate cross-domain transfer.