Average correlation orthogonal matching pursuit algorithm compressed sensing

A technology of orthogonal matching tracking and compressed sensing, applied in electrical components, code conversion, etc., can solve problems such as improving reconstruction performance, and achieve the effect of improving reconstruction performance

Inactive Publication Date: 2017-03-29
NANKAI UNIV
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Problems solved by technology

[0008] The purpose of the present invention solves the bottleneck problem of further improving the reconstruction performance of the traditional greedy algorithm based on the principle of dependency, and proposes an average correlation orthogonal matching pursuit algorithm based on compressed sensing

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  • Average correlation orthogonal matching pursuit algorithm compressed sensing
  • Average correlation orthogonal matching pursuit algorithm compressed sensing
  • Average correlation orthogonal matching pursuit algorithm compressed sensing

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Embodiment Construction

[0033] In order to express the embodiment, significance and advantages of the present invention more clearly, the present invention will be described in more detail below in conjunction with the accompanying drawings, comparison diagrams of reconstruction effects, and theoretical analysis.

[0034] figure 1 It is a flow chart of an average correlation orthogonal matching pursuit algorithm based on compressed sensing proposed by the present invention. The specific flow of the algorithm is as follows:

[0035] (1) Input: perception matrix ΦM×N , measured value y, sparsity K, selected step size S, candidate step size S′, termination parameter ε, correlation set size parameter β 1 , the scaling parameter β 2 , excluding the set size parameter n M , the correlation threshold parameter η M ;

[0036] (2) Initialization: number of iterations k=0, auxiliary flag k'=0, initial support set initial residual r 0 = y, the maximum number of iterations k max =min(K,M / S);

[0037] (3...

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Abstract

The invention discloses an average correlation orthogonal matching pursuit algorithm compressed sensing and belongs to the field of compressed sensing signal processing. According to the algorithm, the problem that the refactoring precision is low when the correlation degree between a last-time iteration residual and atoms in a measurement matrix is taken as a greedy algorithm for a standard of selecting atoms is solved. The invention provides an auxiliary method for selecting the atoms, namely, whether the atoms are selected or not is inspected through utilization of average values of correlations between a plurality of atoms and the residual, wherein the plurality of atoms have relatively high correlation with the atoms. According to the algorithm, the atoms are selected through comprehensive utilization of a traditional method of selecting the atoms according to the correlations between the atoms themselves and the residual, and the auxiliary method. Compared with the traditional greedy algorithm, the algorithm has a great advantage over aspects of an accurate refactoring probability and an average refactoring error, and the application of the compressed sensing in practice can be effectively improved.

Description

[0001] 【Technical field】 [0002] The invention relates to an average correlation orthogonal matching pursuit algorithm based on compressed sensing, which belongs to the field of compressed sensing signal processing. [0003] 【Background technique】 [0004] Compressed sensing is a new sampling theory proposed in recent years. Its advantage lies in the compression of the signal during the signal acquisition process. Unlike traditional Nyquist sampling, the sampling rate of compressed sensing is determined by the sparsity of the original signal. Although the sampling rate becomes lower, the inherently sparse natural signal can still be accurately recovered by the reconstruction algorithm. The feature of compressed sensing can effectively reduce the power consumption of the sampling link, making compressed sensing have broad application prospects in many fields. [0005] How to use the perception matrix Φ and the measured value y to restore the unknown sparse signal is a problem...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/30
CPCH03M7/30
Inventor 孙桂玲王锋李洲周郑博文
Owner NANKAI UNIV
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