Dynamic Nodes Based Cooperative Positioning of D2D Systems in GNSS-Denied Environments

This letter presents a novel mobility-aware device-to-device (D2D) cooperative positioning scheme for dynamic nodes. We employ the Gauss-Markov mobility model to represent and simulate the movement of mobile nodes. Initially, we create a Euclidean distance tensor (EDT) by combining Euclidean distanc...

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Bibliographic Details
Published in:IEEE sensors letters 2023-10, Vol.7 (10), p.1-4
Main Authors: Binny, Neha, C, Abdul Rahim V, S, Chris Prema
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
cooperative positioning
device-to-device (D2D)
Device-to-device communication
Euclidean distance
Euclidean distance tensor (EDT)
Euclidean geometry
Gauss-Markov mobility model
Global navigation satellite system
high accuracy low rank tensor completion (HaLRTC)
Mathematical analysis
Missing data
Mobile nodes
Mobility models
Nodes
Sensor signal processing
Sensors
Tensors
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Summary:This letter presents a novel mobility-aware device-to-device (D2D) cooperative positioning scheme for dynamic nodes. We employ the Gauss-Markov mobility model to represent and simulate the movement of mobile nodes. Initially, we create a Euclidean distance tensor (EDT) by combining Euclidean distance matrices (EDMs) collected at various time stamps. The nonline of sight (NLoS) conditions in global navigation satellite system (GNSS)-Denied environments lead to missing data in EDM which results in inaccurate positioning of D2D nodes. To address this issue, we utilize the high accuracy low rank tensor completion (HaLRTC) algorithm to effectively fill in the missing entries within the EDMs for each time stamp. Subsequently, we utilize multidimensional scaling (MDS) to derive the relative positions of the nodes. While the locations of the anchor nodes remain fixed, we employ Procrustes analysis (PA) to determine the global positions of the unknown nodes. Simulation results demonstrate that our proposed approach achieves a localization error of 0.2017 m with a sparsity level of 90%, along with a ranging error rate of 20%, thus offering high accuracy.
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2023.3312991