.png){kind=icon}
.png){kind=icon}
Return
An Unsupervised Transfer Learning Method for UWB Ranging Error Mitigation
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2023.3325288.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Distance estimation using ultra-wideband transmissions suffers from significant accuracy loss in non-line-of-sight propagation environments. Prevailing machine learning-based error mitigation algorithms are often environment- or device-dependent. This letter proposes an unsupervised transfer learning method based on domain adversarial training and adaptive encoder-decoder, which can retain the ranging accuracy when the environment or the device changes with low data collection workload. Experimental results show that the proposed scheme can achieve a nearly supervised-level error mitigation performance in cross-environment and cross-device applications.
Keywords:
Distance measurement
Training
Transfer learning
Feature extraction
Decoding
Adaptation models
Ultra wideband technology
Ultra-wideband
ranging error mitigation
machine learning
transfer learning