Ultra Wideband
Ultra-wideband (UWB) technology offers high-accuracy localization capabilities, primarily focusing on overcoming challenges like multipath interference and non-line-of-sight (NLOS) propagation. Current research emphasizes improving UWB's accuracy and robustness in complex, large-scale environments through techniques such as machine learning (e.g., deep learning, Kalman filtering variants), sensor fusion (integrating UWB with LiDAR, IMU, or cameras), and novel signal processing methods for NLOS mitigation and ranging error correction. This work has significant implications for various applications, including robotics, autonomous navigation, human-computer interaction, and healthcare, by enabling precise and reliable positioning in diverse settings.
Papers
An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning
Eisa Hedayati, Fatemeh Safari, George Verghese, Vito R. Ciancia, Daniel K. Sodickson, Seena Dehkharghani, Leeor Alon
Fast Localization and Tracking in City-Scale UWB Networks
Nakul Garg, Irtaza Shahid, Ramanujan K Sheshadri, Karthikeyan Sundaresan, Nirupam Roy
WiDEVIEW: An UltraWideBand and Vision Dataset for Deciphering Pedestrian-Vehicle Interactions
Jia Huang, Alvika Gautam, Junghun Choi, Srikanth Saripalli
Analysis on Multi-robot Relative 6-DOF Pose Estimation Error Based on UWB Range
Xinran Li, Shuaikang Zheng, Pengcheng Zheng, Haifeng Zhang, Zhitian Li, Xudong Zou
Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation
Yuxiao Li, Santiago Mazuelas, Yuan Shen
Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
Yuxiao Li, Santiago Mazuelas, Yuan Shen
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Yuxiao Li, Santiago Mazuelas, Yuan Shen