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Distributed compressed sensing (DCS), exploiting the correlation among multiple signals, enjoys the advantage of reduced number of measurements. This paper considers a type of joint sparsity model in DCS, where each signal contains a common component and an innovation component. In order to reduce the transmission cost, the measurements are derived as the sign information of the compressed samples...
Reconstruction algorithm is a significant research field of compressed sensing (CS). Among existing algorithms, regularized orthogonal matching pursuit (ROMP) enjoys the merit of implementing fast recovery procedures. Recent studies have recognized that sparse signals have special sparse structure, which is useful for reconstruction as prior information. In this paper, by utilizing the sparse tree...
For multi-node networks, a distributed compressed sensing scheme based on one-bit quantization is proposed, where signals at each node consist of common component and innovation component. To reduce the transmission cost, each node derives the measurements as the sign information of the compressed samples by using one-bit quantization. Based on the received sign information from different nodes, two...
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