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Alternate Iterative Estimation Method for Sufficiently Sparse Source Signals in Distributed Compressive Sensing

A technology of compressed sensing and alternate iteration, applied in the direction of code conversion, electrical components, etc., can solve the problem of low precision of the source signal, achieve the effect of reducing complexity and improving efficiency

Active Publication Date: 2017-06-16
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0022] The present invention is mainly aimed at the estimation of the fully sparse source signal in the sparse source signal. In order to solve the problem in the prior art, the accuracy of the source signal is caused by using a general reconstruction algorithm to reconstruct the source signal because the characteristics of the mixed signal compression observation value are not considered. low problem, and propose an alternate iterative estimation method for sufficiently sparse source signals in distributed compressed sensing

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  • Alternate Iterative Estimation Method for Sufficiently Sparse Source Signals in Distributed Compressive Sensing
  • Alternate Iterative Estimation Method for Sufficiently Sparse Source Signals in Distributed Compressive Sensing
  • Alternate Iterative Estimation Method for Sufficiently Sparse Source Signals in Distributed Compressive Sensing

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specific Embodiment approach 1

[0053] Specific implementation mode 1: Alternate iterative estimation method of sparse source signal in distributed compressed sensing, such as figure 2 As shown, the reconstruction system part in the figure adopts the iterative estimation method of the present invention, and the iterative estimation method can directly separate and obtain a sufficiently sparse source signal without reconstructing the mixed signal. The alternate iterative estimation method includes the following steps:

[0054] Step 1. Obtain the observation signal Y and convert it into a vector form: the CS observation system in the figure is to collect the mixed signal x i Obtain the observed signal y i , where i∈{1,2,..,P}, P represents the number of channels of the mixed signal, each channel is observed independently, the observation matrix is ​​Φ, and the observation signal y i Expressed as formula (7),

[0055] the y i = Φx i (7)

[0056] where x i has length N, y i The length of is M, Φ is an M...

specific Embodiment approach 2

[0080] Embodiment 2: This embodiment differs from Embodiment 1 in that: the observation matrix Φ described in Step 1 follows a Gaussian distribution.

specific Embodiment approach 3

[0081] Specific embodiment three: the difference between this embodiment and specific embodiment one is: when the number of iterations is less than 20, the iteration step size λ described in step 4 is 0.01; when the number of iterations is greater than 20, the The iteration step size λ is taken as 0.001.

[0082] The beneficial effects of the present invention will be further elaborated below in conjunction with specific experiments.

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Abstract

The invention relates to an alternate iteration estimation method for sufficient sparse source signals in distributed compressed sensing, in particular to a method for reconstructing the source signals from compressed observed values of mixed signals. The problem that in the prior art, because characteristics of the compressed observed values of the mixed signals are not considered, the source signals are reconstructed by adopting the method of recovering the mixed signals first and then separating the source signals, and the source signals are low in precision is solved. According to the alternate iteration estimation method, under a distributed compressed sensing frame, characteristics of the sufficient sparse source signals are used sufficiently, the source signals are estimated from the observed values of the mixed signals through the alternate estimation method, and in each iteration process, estimation values of the source signals are obtained through direct recovering on the premise that the mixed signals are not reconstructed. According to the method, the source signals are separated from the observed values of the mixed signals in a signal compressed domain, and complexity of the separating process is reduced.

Description

technical field [0001] The invention relates to the field of distributed compressed sensing and the field of blind source separation, in particular to a method for recovering fully sparse source signals from the observed values ​​of mixed signals under the framework of distributed compressed sensing signal processing. Background technique [0002] Traditional signal sampling is generally based on the Nyquist sampling theorem, that is, the sampling rate must be at least twice the highest frequency of the signal, so that the discrete data obtained by sampling can be used to separate the source signal without distortion. However, with the development of information technology, the signal processing framework based on the Nyquist sampling theorem puts forward higher requirements on the sampling rate and processing speed of the front-end analog-to-digital converter (ADC), and also puts forward higher requirements on the transmission of back-end information. , The storage link has...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03M7/30
Inventor 徐红伟付宁殷聪如张毅刚彭喜元
Owner HARBIN INST OF TECH