Segmented self-adaptive regularized matching pursuit reconstruction method based on threshold

Abstract

The invention relates to the technical field of compression perception and specifically relates to a segmented self-adaptive regularized matching pursuit reconstruction method based on a threshold. The method includes: setting initial state values of different parameters in a sparse signal reconstruction process; calculating the inner products of an iterative allowance r and each column of a sensing array Phi, that is correlation coefficients; finding atoms which satisfy a condition in the sensing array; storing in a subscript set J; ranking correlation coefficients of atoms corresponding to subscripts in the subscript set J from large to small; updating a support set which represents original signals; performing signal approximation through adoption of a least square method and updating the allowance; and performing iteration determination. The segmented self-adaptive regularized matching pursuit reconstruction method based on the threshold combines segmented self-adaptive atom selecting and regularization ideas. The method does not need sparseness as a prior condition in a signal reconstruction process and is self adaptive to approximation sparseness information and capable of constructing the support set accurately and completing precise signal reconstruction and the precise reconstruction rate is higher than the prior method of the same kinds so that the method is higher in practical applicability.

Description

technical field [0001] The invention belongs to the technical field of compressed sensing, and in particular relates to a threshold-based segmentation adaptive regularization matching tracking reconstruction method. Background technique [0002] Compressed Sensing is a new theory of sampling sparse or compressible signals while doing proper compression. Theory shows that random sampling of sparse or compressible signals at a frequency lower than or even far lower than the Nyquist standard can still accurately reconstruct the original signal. The outstanding advantage of this theory is that it combines data acquisition and data processing into one, which saves hardware resources and greatly reduces software processing time and storage space. [0003] Different from traditional uniform sampling, the core of compressive sensing is a linear measurement process. Suppose x is the original signal of length N, and x is K sparse (or compressible) signal, which means that x can be r...

AI Technical Summary

Problems solved by technology

[0011] but minimum l 0 The norm problem is an NP-hard problem that requires exhaustive enumeration of all non-zero values ​​in s permutations are possible, so it is impossible to solve

However, such algorithms require sparsity as a priori information when reconstructing, and the sparsity is usually unknown in practical applications, and inaccurate estimation of sparsity will lead to serious problems of unstable reconstructed signals

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