Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix

A Bayesian compression and observation matrix technology, applied in the field of information and communication, can solve the problem of low precision of compressed sensing signal recovery method

Active Publication Date: 2014-06-04
HARBIN INST OF TECH
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Problems solved by technology

[0006] The present invention is to solve the problem of low accuracy of the existing compressed sensing signal recovery

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  • Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix
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  • Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix

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specific Embodiment approach 1

[0086] Embodiment 1. A Bayesian compressed sensing signal recovery method based on an adaptive observation matrix,

[0087] Compressive sensing theory includes the following three steps:

[0088] 1), the N×1-dimensional unknown signal f is sparse under the linear basis Ψ(N×N), namely:

[0089] f=Ψw (2)

[0090] Among them: w is an N×1-dimensional sparse signal, that is, most of its coefficients are 0;

[0091] 2) Use the M×N dimensional observation matrix Φ′ to obtain observation values:

[0092] y=Φ′f=Φ′Ψw=Φw (1)

[0093] Among them: y is the measured value of M×1 dimension, Φ=Φ′Ψ is the perception matrix of M×N dimension;

[0094] 3) Given Φ′, Ψ, and y, choose an appropriate restoration algorithm to restore f:

[0095] f ^ = Φ ′ - 1 y

[0096] 1. Design method of observation matrix

[0097] The design of the observation matri...

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Abstract

The invention provides a method for Bayes compressed sensing signal recovery based on a self-adaptive measurement matrix and relates to the field of the information and communication technology. The method aims at solving the problem that an existing compressed sensing signal recovery method is low in accuracy. Based on the design of the self-adaptive measurement matrix in compressed sensing and combined with the Bayes compressed sensing algorithm, a design scheme of the compressed sensing method is obtained. The method is characterized in that the designed measurement matrix can be generated in a self-adaptive mode according to different signals, the purposes of determinacy and storage of the matrix are both achieved, and combined with the Bayes compressed sensing recovery algorithm of a relevant vector machine, the priority of a layered structure is introduced. The design scheme passes simulation verification, it is confirmed that the good signal recovery effect can be obtained, and the error range of recovered signals can be evaluated. The method is suitable for wireless signal transmission occasions in the information and communication technology.

Description

technical field [0001] The invention relates to the technical field of information and communication, in particular to a Bayesian compressed sensing signal restoration method. Background technique [0002] Compressed sensing technology can sample the signal at a very low sampling rate and restore the original signal with high quality, which solves the huge pressure of signal sampling, transmission and storage caused by the huge demand for information. The design of the observation matrix and the restoration method are two critical parts in the compressive sensing process. [0003] The observation matrix is ​​mainly divided into random observation matrix and deterministic observation matrix. Random observation matrix has high recovery accuracy, but its uncertainty will bring difficulties to matrix storage and hardware implementation; deterministic observation matrix can save storage space and is easy to implement in hardware, but its recovery effect is poor. Adaptive observ...

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Application Information

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IPC IPC(8): H03M7/30
Inventor 郭庆贾敏王薇王学东顾学迈王雪贾丹
Owner HARBIN INST OF TECH
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