Compressed sensing reconstruction method for signals with unknown sparseness

Abstract

The invention discloses a compressed sensing reconstruction method for signals with unknown sparseness. The method comprises the steps of obtaining the estimated value of the sparseness of an original sparse signal by utilizing an estimation method based on a matching test and according to an observation vector and a measurement matrix; then according to the estimated value of the sparseness, backtracking and reconstructing to obtain a relatively accurate support set and a relatively accurate margin vector by using an iterative loop mode; obtaining a final extended support set by using the iterative loop mode according to the relatively accurate support set and the relatively accurate margin vector; and finally obtaining a compressed sensing reconstruction signal of the original sparse signal according to the final extended support set. The compressed sensing reconstruction method for signals with unknown sparseness has the advantages of being capable of accurately reconstructing the signals with unknown sparseness, and being low in computation amount and high in practicability.

Description

technical field [0001] The invention relates to a signal reconstruction method in compressed sensing, in particular to a compressed sensing reconstruction method for unknown sparsity signals. Background technique [0002] In traditional signal processing, it is first necessary to sample the signal, and then compress, store, and transmit the sampled data obtained after sampling. In the process of compression, a large amount of sampled data will be discarded, although these sampled data are important to the original Signal is some unimportant or just redundant information, but the compression of these sampling data wastes a lot of resources. In response to this problem, Donoho, Candes and Tao et al. proposed the Compressed Sensing (CS) theory in 2006, which is a new type of signal sampling theory. Compressed sensing theory shows that the signal can be sampled at a rate lower than the Nyquist rate if the signal is compressible or sparse in the transform domain, and the signal ...

AI Technical Summary

Problems solved by technology

Representative classic greedy pursuit algorithms include matching pursuit (Matching Pursuit, MP) algorithm, orthogonal matching pursuit (Orthogonal Matching Pursuit, OMP) algorithm, regularized orthogonal matching pursuit (Regularized OMP, ROMP) algorithm, etc. With high sparsity, signal reconstruction is not very effective

The subspace pursuit (Subspace Pursuit, SP) algorithm introduces the idea of ​​backtracking, and the atoms in the support set are inspected and corrected during each iteration, so that the algorithm has high reconstruction accuracy and low computational complexity. However, the The algorithm requires known sparsity, which is difficult to meet in practical applications, and the estimation of sparsity often has errors, which will make the algorithm unable to accurately reconstruct the signal

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