Domain-Adversarial Learning for UWB NLOS Identification in Dynamic Obstacle Environments

• Published in: IEEE sensors journal Vol. 25; no. 13; pp. 23312 - 23325
• Main Authors: Wang, Qiu, Chen, Ming-Song, Yan, Xin, Lin, Yong-Cheng, Li, Kai, Liu, Jia-Jie, Zhang, Chi-Zhou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adversarial neural network; Barriers; Channel impulse response; Classification; Data mining; Distance measurement; domain adaption; Feature extraction; Impulse response; Location awareness; Machine learning; Neural networks; nonline-of-sight (NLOS) identification; Occlusion; Sensors; Testing; Training; Transfer learning; transfer learning (TL); Ultrawideband; ultrawideband (UWB)
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3491178

Ultrawideband (UWB) radio frequency positioning technology has found extensive applications in indoor and outdoor localization due to its strong anti-interference capabilities, high penetration power, and precise measurement accuracy. However, its positioning accuracy significantly decreases under nonline-of-sight (NLOS) conditions, particularly in dynamic NLOS environments. Therefore, it is essential to identify NLOS propagation and mitigate its associated errors. To overcome these issues, this study proposes a novel enhanced domain-adversarial neural network for UWB signal occlusion recognition (EDANN-SOR). EDANN-SOR integrates hybrid manually extracted channel impulse response (CIR) features, effectively aligning the source and target domains and extracting discriminative and domain-invariant UWB CIR features in dynamic NLOS environments. Through adversarial learning, our method achieves high generalization with minimal CIR sample requirements, reducing the need for extensive data collection and significantly enhancing robustness in distance measurement and positioning under dynamic NLOS conditions. In contrast to state-of-the-art NLOS identification and transfer learning (TL) techniques, our method excels in both binary and multiclass NLOS classification. Specifically, it achieves an accuracy of over 97.36% in conventional LOS/NLOS binary classification across scenarios and 97.07% in multiclass classification. The integration of manually extracted features has been proved effective in improving the EDANN-SOR model's ability to distinguish among LOS, human, and glass obstacles.