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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/19083

Title: A Distributed Sensor-based Recursive Framework for DoA Estimation and Geolocation
Authors: Jiang, Lei
Keerativoranan, Nopphon
Matsumoto, Tad
Takada, Jun-ichi
Keywords: Direction-of-arrival (DoA)
extended Kalman filter (EKF)
eigenvalue decomposition (EVD)
factor graph (FG)
distributed sensors
low-rank adaptive filter (LORAF)
Issue Date: 2024-07-09
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE Access
Start page: 1
End page: 1
DOI: 10.1109/ACCESS.2024.3424216
Abstract: This paper proposes a distributed sensor-based RECursive Subspace and Factor Graph (RECSaFG) framework for direction-of-arrival (DoA) estimation and geolocation of a fast-moving target. The whole framework includes two recursive processes: (1) DoA estimation and tracking by 2-dimensional (2D) smoothing-based recursive subspace technique using low rank adaptive filter (LORAF); (2) Factor graph (FG)-based geolocation and tracking network utilizing an extended Kalman filter (EKF) which takes into account the target’s position and velocity, and updates them as well as the acceleration information. In (1), the recursive subspace technique aims to fully utilize sample size insufficiency due to the fast-moving target and to recover the rank deficiency incurred by the coherent signal components. In (2), the estimated DoA and target velocity information obtained by (1) is considered as input to the unified FG implemented by EKF for geolocation and tracking (FG-GE-TR) of the target position. By integrating these two processes, the REC-SaFG framework promises significant improvements in the accuracy and efficiency of geolocation and tracking systems, particularly in environments characterized by a fast-moving target and the need for high-resolution tracking.
Rights: Lei Jiang, Nopphon Keerativoranan, Tad Matsumoto, Jun-ichi Takada, IEEE Access (Early Access), 2024. DOI: 10.1109/ACCESS.2024.3424216. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
URI: http://hdl.handle.net/10119/19083
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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