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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:MILCOM IEEE Military Communications Conference S. 528 - 532
Hauptverfasser: Li, Yuxiao, Mazuelas, Santiago, Shen, Yuan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 29.11.2021
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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