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A Distributed Sensor-Based Recursive Framework for DoA Estimation and Geolocation
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DOI：10.1109/ACCESS.2024.3424216.png)
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Abstract
En 中文
This paper proposes a distributed sensor-based RECursive Subspace and Factor Graph (REC-SaFG) 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.
Keywords:
Geology
Direction-of-arrival estimation
Estimation
Vectors
Target tracking
Accuracy
Aircraft
Location awareness
Eigenvalues and eigenfunctions
Decentralized control
Sensor systems
Adaptive filters
Direction-of-arrival (DoA)
geolocation
tracking
extended Kalman filter (EKF)
subspace
eigenvalue decomposition (EVD)
factor graph (FG)
distributed sensors
low-rank adaptive filter (LORAF)