Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation

Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learning-based methods for error mitigation have shown great performance improvement via exploiting high semantic featu...

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Vydáno v:MILCOM IEEE Military Communications Conference s. 528 - 532
Hlavní autoři: Li, Yuxiao, Mazuelas, Santiago, Shen, Yuan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 29.11.2021
Témata:
Deep learning
Distance measurement
generalized expectation-maximization algorithm
Probabilistic logic
ranging error mitigation
Semantics
Signal processing
Signal processing algorithms
Supervised learning
UWB radio
weakly supervised Learning
ISSN:2155-7586
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Shrnutí:Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learning-based methods for error mitigation have shown great performance improvement via exploiting high semantic features from raw data. However, these methods rely heavily on fully labeled data, leading to a high cost for data acquisition. We present a learning framework based on weak supervision for UWB ranging error mitigation. Specifically, we propose a deep learning method based on the generalized expectation-maximization (GEM) algorithm for robust UWB ranging error mitigation under weak supervision. Such method integrate probabilistic modeling into the deep learning scheme, and adopt weakly supervised labels as prior information. Extensive experiments in various supervision scenarios illustrate the superiority of the proposed method.
ISSN:2155-7586
DOI:10.1109/MILCOM52596.2021.9653015