Variable step size distributed compressed sensing reconstruction method based on recurrent neural network

A technology of cyclic neural network and compressed sensing, which is applied in the field of variable step size distributed compressed sensing reconstruction, can solve the problems that hinder the application of distributed compressed sensing model, and achieve the effect of broadening the range

Inactive Publication Date: 2017-11-03
HUBEI UNIV OF TECH
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However, few signals in the real world meet this requirement, hinderi

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[0035] In order to facilitate the understanding and implementation of the present invention by those of ordinary skill in the art, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, but not to limit the present invention.

[0036]An embodiment of the present invention provides a variable step size distributed compressed sensing reconstruction method based on a cyclic neural network, comprising the following steps:

[0037] 1) Train the LSTM network.

[0038] The LSTM network structure with peephole connection proposed by Gers and Schmidhuber in 2000 is adopted, which is used to select the best atoms in the reconstruction process. Before using LSTM to select atoms, it is necessary to use data to train the network parameters. The training method adopts Nesterov algorithm. The steps of training the network are...

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Abstract

The invention belongs to the field of distributed compressed sensing reconstruction technology, and particularly relates to a variable step size distributed compressed sensing reconstruction method based on a recurrent neural network. The method comprises the following steps: acquiring structural information of a vector to be reconstructed of each channel by using the recurrent neural network, obtaining each non-zero conditional probability of the vector in each channel, then estimating an optimal atom of the current iteration, and then determining a value of a non-zero term of each channel by solving a least square problem, and completing the reconstruction of signals. According to the method, non-combined sparse multi-channel signals can be reconstructed, and meanwhile, the computational complexity of the coding end cannot be increased.

Description

technical field [0001] The invention belongs to the technical field of distributed compressed sensing reconstruction, and in particular relates to a variable-step distributed compressed sensing reconstruction method based on a cyclic neural network. Background technique [0002] Compared with the traditional Nyquist sampling theory, the compressive sensing technology that can solve the sparse solution of the underdetermined linear system can reconstruct the signal with a sampling rate much lower than the latter, so it has attracted extensive attention from the academic community in recent years. However, compressed sensing technology only considers the processing of single-channel signals. When multi-channel signal reconstruction is required, compressed sensing technology does not use signal correlation to improve reconstruction speed or reconstruction accuracy. In order to make full use of the structural relationship within and between multi-channel signals, a distributed c...

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Application Information

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IPC IPC(8): H03M7/30G06N3/04G06N3/08
CPCH03M7/3062G06N3/08G06N3/045
Inventor 曾春艳武明虎万相奎熊炜刘敏赵楠朱莉李利荣王娟饶哲恒
Owner HUBEI UNIV OF TECH
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