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Diffusion-based Kalman iterative thresholding for compressed sampling recovery over network
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DOI：10.1016/j.sigpro.2022.108750.png)
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Abstract
En 中文
Network-based CS recovery is used for faster processing of large-scale data, as well as for sensor net-works where the observation vector and sampling matrix are distributed. In this paper, we propose a distributed CS recovery algorithm, called DKIST, which is based on three concepts: diffusion strategy, it-erative thresholding, and extended Kalman filtering. We investigate that the estimation is unbiased and minimum variance, and the entire network is stable. Also, we proposed some modifications on DKIST to lower communication bit rate and computation of complexity. Simulation results show that the proposed algorithms outperform the competing distributed CS recovery methods performance in terms of accuracy and speed of convergence.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Compressed sampling
Distributed CS recovery
Diffusion strategies
Kalman iterative soft thresholding