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Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Improving UWB Based Indoor Positioning in Industrial Environments Through Machine Learning
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Experimental researches on an UWB NLOS identification method based on machine learning
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Real-time identification of NLOS range measurements for enhanced UWB localization
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An Analytical Study of Time of Flight Error Estimation in Two-Way Ranging Methods
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Convolutional Neural Networks for Position Estimation in TDoA-Based Locating Systems
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NLOS Detection and Mitigation in Differential Localization Topologies Based on UWB Devices
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Accuracy Assessment and Learned Error Mitigation of UWB ToF Ranging
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A Bidirectional Object Tracking and Navigation System using a True-Range Multilateration Method
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NLOS ranging error mitigation in a distributed positioning algorithm for indoor UWB ad-hoc networks
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Ranging With Ultrawide Bandwidth Signals in Multipath Environments
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NLOS identification and mitigation for localization based on UWB experimental data
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A new low complexity NLOS identification approach based on UWB energy detection
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Real-time room occupancy estimation with Bayesian machine learning using a single PIR sensor and microcontroller
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A Machine Learning Approach to Ranging Error Mitigation for UWB Localization
inferredcites →https://doi.org/10.1109/tvt.2017.2780121openalex-snapshot
Deep Learning Based NLOS Identification With Commodity WLAN Devices
inferredcites →https://doi.org/10.1109/twc.2014.2372341openalex-snapshot
Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength
inferredcites →https://doi.org/10.1109/twc.2015.2496584openalex-snapshot
Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity
inferredcites →https://doi.org/10.1109/twc.2020.2967726openalex-snapshot
Machine Learning-Enabled LOS/NLOS Identification for MIMO Systems in Dynamic Environments
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Decision theoretic framework for NLOS identification
inferredcites →https://doi.org/10.1109/wcnc.2007.296openalex-snapshot
NLOS Identification and Mitigation for UWB Localization Systems
inferredcites →https://doi.org/10.1109/wpnc.2007.353628openalex-snapshot
NLOS detection algorithms for Ultra-Wideband localization
inferredcites →https://doi.org/10.1109/wpnc.2008.4510359openalex-snapshot
Performance comparison of TOA and TDOA based location estimation algorithms in LOS environment
inferredcites →https://doi.org/10.1109/wpnc47567.2019.8970249openalex-snapshot
A Comparative Study of UWB-based True-Range Positioning Algorithms using Experimental Data
inferredcites →https://doi.org/10.1126/science.1096796openalex-snapshot
Metamaterials and Negative Refractive Index
inferredcites →https://doi.org/10.23919/mikon.2018.8405190openalex-snapshot
Detection of direct path component absence in NLOS UWB channel
inferredcites →https://doi.org/10.3390/app10113980openalex-snapshot
Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
inferredcites →https://doi.org/10.3390/s19030616openalex-snapshot
Numerical and Experimental Evaluation of Error Estimation for Two-Way Ranging Methods
inferredcites →https://doi.org/10.3390/s19163464openalex-snapshot
NLOS Identification and Mitigation Using Low-Cost UWB Devices
inferredcites →https://doi.org/10.3390/s19194229openalex-snapshot
Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs
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Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs
inferred← citeshttps://doi.org/10.48550/arxiv.2302.07706openalex-snapshot
Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-deprived Zones
inferred← citeshttps://doi.org/10.70675/701d8c83zdadaz43c1zb62cza9c2645b7212openalex-snapshot
Robust Indoor Localization with RF-Light Fusion in Dynamic Environments
inferred← citeshttps://doi.org/10.70675/b47d8aa1z5e06z4c51zadfbz212a2d2328ffopenalex-snapshot
Localisation en intérieur à bande ultra large (UWB) et Reconnaissance d'actions industrielles
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