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)

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 n...