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EEMD-based compressive sensing method

A technology of compressed sensing and algorithms, applied in the field of information, can solve the problems of EMD methods that cannot correctly separate different feature components, mode aliasing, etc.

Inactive Publication Date: 2016-03-16
HOHAI UNIV
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Problems solved by technology

However, when the signal contains multiple components with significantly different time scales, the EMD method cannot correctly separate the different characteristic components, resulting in mode aliasing

Method used

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Embodiment Construction

[0042] In view of the existing status quo, scholars add white noise to the information, and then aggregate and average to obtain each component. This method is called the aggregate empirical mode decomposition method (ensembleEMD, EEMD). This method can correctly separate the feature components of different scales. .

[0043] The present invention is produced under this background. The method obtains a group of eigenmode functions through EEMD transformation, and then uses the clustering method to cluster the eigenmode functions, adopts the EEMD construction method to construct a learning dictionary, and finally uses the normal The signal is reconstructed by the cross-matching pursuit algorithm, which maximizes the compression rate of the signal and the accuracy of reconstruction.

[0044] The present invention will be further described below in conjunction with accompanying drawing.

[0045] Such as figure 1 As shown, an EEMD-based compressed sensing method includes the fol...

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Abstract

The invention discloses an EEMD-based compressive sensing method which is a novel information processing method, overcomes the defects in the conventional signal compressing process, and can compress information and reconstruct a source signal to the maximum extent. The EEMD-based compressive sensing method comprises the following steps: separating out a signal eigenfunction according to an EEMD method, reconstructing an over-complete dictionary by adopting a K-mean cluster, and then reconstructing a sparse signal according to an orthogonal matching pursuit algorithm.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a method based on EEMD and compressed sensing. Background technique [0002] With the rapid development of microelectronics, communication, network and other disciplines, we have entered the information age. Then the information in the physical world is continuous. To digitize these continuous signals, Shannon sampling theorem limits the sampling frequency too high, so that the amount of digitized data is too large. The importance of data compression in information processing is self-evident. As early as 1959, Hubel studied the visual perception of cats and found that the cells located in the visual cortex of the brain can perform sparse representation of visual information. The study of sparsity has attracted the attention of scholars. An important expression of thought. [0003] Compressed sensing combines acquisition and compression in one step, which greatl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/30
CPCH03M7/55
Inventor 许军才任青文沈振中张卫东
Owner HOHAI UNIV
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