Variable step size regularized adaptive compressed sampling matching pursuit method

Abstract

The invention provides a variable step size regularized adaptive compressed sampling matching pursuit method, specifically relates to a reconstruction method based on compressed sensing, and a solution aims at the prior two problems in the regularized orthogonal matching pursuit (ROMP) method. One of the problems is that the sparseness K is difficult to be obtained in reality, then the method provides that the adaptive idea of the sparse adaptive matching pursuit (SAMP) is applied in ROMP; the other problem is that once an atom which is selected in a support set will not able be deleted, then the method provides that the backtrack idea of the compressed sampling matching pursuit (CoSaMP) is applied in ROMP. In addition, the method adopts a stage orthogonal matching pursuit (StOMP) idea to set the threshold condition so as to stop the iteration, and the problem including lack of accuracy and over estimated caused by the fixed step size of the introduced SAMP is improved. The method provided by the invention greatly improves the reconstruction performance, estimated accuracy and application range of the reconstruction method based on the compressed sampling.

Description

technical field [0001] The present invention mainly relates to the field of compressed sensing (Compressed Sensing, CS), and in particular to the field of CS-based signal reconstruction methods. Background technique [0002] With the rapid development of Internet technology, the current era has entered an era of rapid information development. In people's daily life, they need to deal with a large amount of information, including identification, sampling, storage and transportation of information. At the same time, people's demand for information is also increasing, which puts forward higher requirements for information processing and sampling rate, while traditional information processing methods are based on the Nyquist sampling theorem. , not only to maintain a very high sampling rate, but also to complete the compression, storage and transmission of a large amount of original information, resulting in a great waste of resources; at the same time, it will put forward highe...

AI Technical Summary

Problems solved by technology

However, in practice, the degree of sparsity is usually unpredictable. If some calculation is performed on the observer at the observation end to calculate the degree of sparsity, the calculation amount of the sensor part will increase.

In this case, since the sparsity K cannot be accurately estimated, the reconstruction quality cannot be guaranteed

On the other hand, because ROMP adopts the method of forward tracking to update the support set, once the atom is selected into the support set, it will exist permanently and cannot be deleted, lacking the idea of ​​backtracking, thus causing a lot of waste of resources

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