Effective '0,1' sparse signal compressed sensing reconstruction method

A compressive sensing reconstruction, sparse signal technology, applied in the "0 field"

Active Publication Date: 2014-05-28
CHONGQING UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to solve how to make full use of the special structure of "0, 1" sparse signal, and design an effective compressed sensing reconstruction method of "0, 1" sparse signal, thereby helping to reduce data sampling, storage and transmission. cost problem

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  • Effective '0,1' sparse signal compressed sensing reconstruction method
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[0043] The present invention is further described below by means of examples, and the present invention is not therefore limited to the scope of the examples.

[0044] The technical concept of the present invention and the working process under this concept will be described in detail below in conjunction with the accompanying drawings.

[0045] according to figure 2 As shown, a compressive sensing reconstruction method of "0, 1" sparse signal, including the following steps:

[0046] (1) The bipartite graph model cleverly introduced in graph theory, closely combined with the minimum coverage property of the bipartite graph, and appropriate constraints are added to construct a sparse, uniform and minimum coverage observation matrix. The added constraints are (1.1) the bipartite graph has a total of l=ML edges; (1.2) the number of edges connected to the observed value y=Φx in the bipartite graph is That is, the zero norm of each row of the observation matrix Φ is ||Φ(i,:)|| ...

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Abstract

The invention discloses an effective '0,1' sparse signal compressed sensing reconstruction method. The method mainly comprises a sparse and uniform measurement matrix construction part and an iteration reconstruction order part based on a bipartite graph. According to the method, a bipartite graph model in a graph theory is ingeniously introduced, the minimum cover characteristic of the bipartite graph is closely combined, a constraint condition is appropriately added, and the sparse, uniform and minimally-covered measurement matrix is constructed. Based on the special structure that the '0,1' sparse signals are fully utilized in an iteration reconstruction algorithm based on the bipartite, the connecting line phi ij of the bipartite graph is deleted and a measurement value y is updated through an iteration method, and an original signal reconstruction method is achieved finally. According to the method, the bipartite graph model in the graph theory is introduced in compressed sensing sampling and reconstruction, compared with an l1 norm minimization method, reconstruction errors do not exist, the method can be applied to compressive sampling of neutron pulse sequences, earthquake signals, wireless sensor networks, binary images and the like.

Description

technical field [0001] The invention belongs to the field of compressed sensing technology research and relates to an effective compressed sensing reconstruction method of "0, 1" sparse signals. Background technique [0002] Compressive Sensing (Compressive Sensing) theory was proposed by Candès, Romberg, Donoho and Tao, which includes keywords such as sparse, uncorrelated, random, non-adaptive, nonlinear and non-differentiable. From these keywords, especially the first and second keywords, it can be seen that compressed sensing theory is a subversion of traditional theories. The most exciting performance of this subversion is that it breaks through the Nyquist sampling theorem, and can perfectly restore the original signal in a random sampling manner with fewer data sampling points. [0003] For the N×1 original signal x which itself has the K-sparse property, its compressed sampling can be expressed directly and losslessly as y=Φx. Among them, Φ is the observation matrix...

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Application Information

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IPC IPC(8): H03M7/30
Inventor 李鹏程魏彪冯鹏任勇米德伶
Owner CHONGQING UNIV
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