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Adaptive threshold value iterative reconstruction method for distributed compressed sensing

An adaptive threshold and compressed sensing technology, applied in the field of signal processing, can solve the problems of long reconstruction time, large reconstruction error, and constant step size of the simultaneous orthogonal matching pursuit method, and achieve step size adaptive iteration threshold, The effect of less reconstruction time and reduction of reconstruction errors

Active Publication Date: 2015-02-04
XIANGTAN UNIV
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Problems solved by technology

[0009] Aiming at the problems that the reconstruction time of the simultaneous orthogonal matching tracking method is long, the step size of the simultaneous hard threshold iteration and simultaneous hard threshold tracking methods is unchanged, and the reconstruction error is relatively large, the present invention discloses an adaptive threshold value in distributed compressed sensing. iterative refactoring method

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  • Adaptive threshold value iterative reconstruction method for distributed compressed sensing
  • Adaptive threshold value iterative reconstruction method for distributed compressed sensing
  • Adaptive threshold value iterative reconstruction method for distributed compressed sensing

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specific Embodiment approach 1

[0026] combine figure 1 The specific implementation mode 1 is described as follows:

[0027] Step 1. Input: perception matrix A, observation value Y, maximum iteration number inter_max, iteration termination threshold error, joint sparsity s, iteration initial value x k ;

[0028] Initialization: number of iterations inter=1, parameters τ and μ, support set S={||(A T Y) i || 2 the largest s indices, i∈[1,N]};

[0029] The perception matrix A and the observed value Y are specifically as follows: Observing J signals to obtain the observed value Y: Y=[y 1 the y 2 … y J ], the y j is the observed value of signal j, M is the observed value y j The number of data, j∈[1, J], N is the length of signal acquisition data.

[0030] Step 2. Calculate the adaptive step size τ and the adaptive threshold h, the specific calculation process:

[0031] 1) Calculation parameter rk, rk=sum((Ax k -Y).^2);

[0032] 2) Calculate the adaptive step size τ,

[0033] 3) Calculate ...

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Abstract

The invention provides an adaptive threshold value iterative reconstruction method for distributed compressed sensing, and mainly solves the problems that signal reconstruction time is long, reconstruction errors are large and the like in the prior art. The method includes the steps: (1) calculating an adaptive step and an adaptive threshold value h; (2) calculating an iterative value according to an iterative formula; (3) comparing the iterative value with the calculated adaptive threshold value h to obtain an iterative result; (4) updating a support set and modifying the iterative result; (5) stopping iteration and acquiring estimation signals when meeting an iteration stopping condition, otherwise, continuing iteration. The adaptive threshold value iterative reconstruction method has the advantages of step and threshold value adaptivity, shorter reconstruction time, small reconstruction error and the like.

Description

technical field [0001] The invention relates to a signal reconstruction method, which belongs to the technical field of signal processing. Background technique [0002] Compressive Sensing (CS) breaks through the traditional signal sampling theory of Nyquist sampling theorem. In 2006, CS was proposed by David Donoho and Emmanuel Candes et al. According to the traditional sampling theorem, in the process of converting an analog signal to a digital signal, in order to ensure that the signal is restored without distortion, the sampling frequency must be greater than or equal to twice the maximum frequency of the analog signal. However, compressed sensing is to sample the sparse signal at a rate much lower than the Nyquist sampling rate, and reconstruct the signal through the compressed sensing reconstruction algorithm. Compressed sensing combines the two processes of sampling and compression, which reduces the sampling frequency of the signal and reduces the data storage spac...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/30
Inventor 李哲涛曹斌朱更明田淑娟
Owner XIANGTAN UNIV
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