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Retrospective Genetic Iterative Reconstruction Method in Compressed Sensing

A compressed sensing and iterative reconstruction technology, applied in the field of signal processing

Active Publication Date: 2018-12-28
XIANGTAN UNIV
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AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the traditional greedy reconstruction algorithm needs to know the sparsity K and the subspace tracking algorithm cannot judge whether the newly added atoms in the support set are better, a retrospective genetic iterative reconstruction method in compressed sensing is proposed. Its process is inverse to that of the greedy reconstruction algorithm

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  • Retrospective Genetic Iterative Reconstruction Method in Compressed Sensing

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Embodiment Construction

[0025] According to one aspect of the present invention, as figure 1 Shown, the embodiment of the present invention is as follows:

[0026] Step 1. Set the population and coding scheme:

[0027] 1) The sparse signal θ to be sought is equivalent to the chromosome for population setting, that is, the population is equivalent to the support set ψ of the desired sparse signal θ, and the chromosome is the atom of the support set ψ;

[0028] 2) Assume that the support set is a matrix of N columns and M dimensions, N represents the number of chromosomes, M represents the number of genes in each chromosome, input measurement value y, random Gaussian measurement matrix Φ∈R N×M and the sparse transformation matrix

[0029] 3) Sparse signal in is the transpose of the product of the random Gaussian measurement matrix and the sparse transformation matrix, x 1 The support set is ψ, and the non-zero elements in the atoms of the support set ψ are set to 1, and the initial support set...

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Abstract

The invention discloses a backtracking genetic iterative reconstruction method in compressed sensing. The backtracking genetic iterative reconstruction method comprises the following steps of firstly, initializing a support set of a sparse signal to be solved, carrying out cyclic iterative approximation on optimal position information of the solved sparse signal through genetic operation, such as copying, multipoint crossing, selection and big mutation treatment, carrying out backtracking updating of the support set, and lastly, projecting by utilizing a least square method to obtain amplitude information of each nonzero element of the sparse signal to be solved so as to complete signal reconstruction. According to the backtracking genetic iterative reconstruction method in compressed sensing, the sparse signal to be solved can be precisely reconstructed under the condition of unknown sparseness.

Description

technical field [0001] The invention relates to a signal reconstruction method, which belongs to the technical field of signal processing. Background technique [0002] The Nyquist sampling theorem states that the sampling rate must be more than twice the signal bandwidth to accurately reconstruct the signal. Then, although the data sampled in this way can fully represent the original signal, there is a large redundancy in the sample data. Therefore, data collected by this method often needs to be compressed to save storage space. Compressed sensing (Compressed Sensing, CS), also known as compressed sampling or sparse sampling, breaks through the signal sampling theory of the traditional Nyquist sampling theorem. The theory points out that as long as the signal is sparse in a transform domain, a measurement matrix unrelated to the sparse basis can be used to project the high-dimensional sparse signal onto a low-dimensional space to complete sparse signal compression. The ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03M7/30
Inventor 李哲涛曾红庆朱更明田淑娟裴廷睿
Owner XIANGTAN UNIV