Generation method of sensing matrix for signal compressive sensing

A perception matrix and signal compression technology, applied in the direction of electrical components, code conversion, etc., can solve the problems of no generation of new perception matrix, no way to artificially control or adjust, and no regularity of elements and structures, so as to reduce the solution time, The effect of reducing implementation cost and improving reconstruction speed

Active Publication Date: 2015-05-27
CENT SOUTH UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the elements and structure of the stochastic perception matrix widely used in practical applications have no regularity at all, and there is no way to artificially control or adjust it during the generation process.
There is currently no way to generate a new perception matrix from a known perception matrix

Method used

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  • Generation method of sensing matrix for signal compressive sensing
  • Generation method of sensing matrix for signal compressive sensing
  • Generation method of sensing matrix for signal compressive sensing

Examples

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Embodiment Construction

[0016] The specific implementation of the method of the present invention can be divided into two steps. First, select or generate a perceptual matrix. Taking random perceptual matrix as an example, a random matrix is ​​generated by Gaussian, Bernoulli and other distributions, and it is verified that it is a perceptual matrix that satisfies the CS property; then, it is left multiplied by a specific reversible matrix or right-multiply a specific reversible diagonal matrix (or its row / column full arrangement), and the matrix product obtained in this way is the new perception matrix we need.

[0017] Below we give an example of sparsely reconstructing an image to illustrate the specific implementation of the method.

[0018] (1) Generate a random perceptual matrix A that can realize image sparse reconstruction. exist figure 1 In the process, first run MATLAB software, do wavelet transform on the original image to obtain its sparse representation (white dots represent large coef...

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Abstract

The invention discloses a generation method of a sensing matrix for signal compressive sensing (CS). The generation method comprises the following steps: firstly, selecting a determinated sensing matrix or generating a random sensing matrix; then performing left multiplication on a specific reversible matrix or right multiplication on a specific reversible diagonal matrix or full permutation of lines / rows of the specific reversible diagonal matrix to obtain a matrix product which is the required novel sensing matrix. Based on conventional sensing matrixes, the structures or elements are adjusted to generate the sensing matrix with certain regularity, and meanwhile, the CS attribute is unchanged. Particularly for the determinated sensing matrix, the invention provides a very convenient generation or extension new way. The sensing matrix with certain regularity on structure or element facilitates improvement of a CS measurement system in the aspects of compressive sampling frequency, signal reconstruction speed, implementation cost and the like.

Description

technical field [0001] The invention relates to the field of compressed sensing of signal processing, in particular to a method for generating a sensing matrix for signal compressed sensing. Background technique [0002] Compressed sensing (Compressed or Compressive Sensing, hereinafter referred to as CS) was originally proposed in 2006 by Donoho, Candes, Romberg and Tao et al. as a new framework for signal acquisition and sensor design. CS is also sometimes called Compressive Sampling or Sparse Recovery, which was rated as one of the top ten scientific and technological advances in 2007 by the American Technology Review. [0003] In recent years, due to the massive sensor data caused by the progress of sensor hardware and acquisition technology, data processing, communication and storage have become bottlenecks. The Shannon / Nyquist sampling theorem states that in order to losslessly sample a signal, the sampling rate must be at least twice the signal bandwidth. In many ap...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/40
Inventor 谭冠政易佳望谭冠军
Owner CENT SOUTH UNIV
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