1 背景

早上从邮箱看到一篇论文《UWB NLOS Identification and Mitigation Based on Gramian Angular Field and Parallel Deep Learning Model》，链接https://ieeexplore.ieee.org/abstract/document/10286374/。

2 分析论文摘要

原文
Ultra-wideband (UWB) wireless localization technology has been widely applied in the field of indoor localization due to its good ability of noise resistance, strong penetration and high measurement accuracy. However, the performance of UWB-based localization technology becomes poor when suffering from non-line-of-sight (NLOS) propagation conditions. Thus, it is necessary to identify NLOS propagation and mitigate the NLOS error. In this paper, a novel NLOS identification and mitigation method based on multi-inputs parallel deep learning model and Gramian Angular Field (GAF) is proposed. We utilize GAF to transform 1-dimension channel impulse response (