Distributed Adaptive Thresholding Graph Recursive Least Squares Algorithm

In this paper, we present a novel approach for the reconstruction of sparse graph signals using a distributed adaptive thresholding recursive least squares algorithm. Our proposed scheme leverages signal measurements across multiple time steps to estimate the underlying graph signal. To achieve this...

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Veröffentlicht in:Circuits, systems, and signal processing Jg. 43; H. 8; S. 4952 - 4967
Hauptverfasser: Maleki, Nafiseh, Azghani, Masoumeh, Sadeghi, Nasser
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2024
Springer Nature B.V
Schlagworte:
Adaptive algorithms
Algorithms
Circuits and Systems
Cost function
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Least squares method
Recovery
Recursive functions
Signal reconstruction
Signal,Image and Speech Processing
Sparsity
Graph signal processing
Recursive least squares
Adaptive thresholding
ISSN:0278-081X, 1531-5878
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Zusammenfassung:In this paper, we present a novel approach for the reconstruction of sparse graph signals using a distributed adaptive thresholding recursive least squares algorithm. Our proposed scheme leverages signal measurements across multiple time steps to estimate the underlying graph signal. To achieve this, we define a cost function that combines a weighted least squares term and an L 0 norm. The cost function is then minimized using a surrogate minimization scheme and the recursive least squares technique. The resulting algorithm is an adaptive thresholding recursive least squares scheme that effectively recovers sparse graph signals. Additionally, we apply a consensus-based method to enable distributed recovery, and derive recursive updating relations to speed up the algorithm. Our approach offers a significant novelty in the field of graph signal recovery, providing an efficient and accurate solution for reconstructing sparse graph signals. The numerical simulations show the superiority of the proposed algorithm over the other state-of-the-art algorithms from the point of view of the convergence rate and the mean square deviation (MSD) criterion.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-024-02626-0