An Adaptive Compressed Sensing Signal Restoration Method Based on Greedy Algorithm

A compressed sensing and signal recovery technology, applied in the field of signal processing, which can solve the problems that are difficult to be directly applied to unknown sparsity, computationally expensive convex relaxation method is not suitable, and signal sparsity is unknown.

CN104485965BInactive Publication Date: 2017-07-28UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2017-07-28
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses an adaptive compressed sensing signal recovery method based on a greedy algorithm. On the basis of the OMP algorithm, a detector is designed to judge whether there is still a signal component in the remaining vector, and through the hypothesis testing model in the signal detection theory, Calculate the detector, and then give the threshold of the detector through the preset false alarm probability. When the detector is smaller than the given threshold, it can be judged that there is no signal component in the remaining vector. At this time, the algorithm can stop iterating, and Get the restored value of the signal, otherwise continue to iterate. The present invention uses the greedy algorithm as channel estimation, does not depend on the multipath number of the channel; Compared with MDL, the method of the present invention does not need multiple observations, thereby saving communication resources; The method of the present invention has less computing power of the greedy algorithm The advantage is that the greedy algorithm solves the problem of high real-time requirements for sparse signal recovery, and its application in the problem of unknown signal sparsity.
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Description

technical field

[0001] The invention belongs to the field of signal processing, in particular to an adaptive compressed sensing method. Background technique

[0002] Compressed sensing is a signal processing method for dealing with sparse vectors, and it is one of the most important theories proposed in this century. Compressed sensing technology can recover sparse signals from fewer sample points than the Nyquist sampling rate, ie y=Ax+e, where, is the observation vector, through the observation matrix for a k-sparse signal For observation, the dimension of the observation vector y is much smaller than the dimension of the signal x, m<n, and the compressed sensing technology can recover the signal x from the observation vector y. According to the compressed sensing technology, the signal can be directly compressed when it is acquired, which saves the complexity of sampling and saves the storage space. The so-called sparse signal means that most of the positions of...

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Embodiment Construction

[0065] The idea of ​​the invention is as follows: the adaptive compressed sensing signal recovery method based on the greedy algorithm designs a judgment residual vector r on the basis of the OMP algorithm t Is there still a detector T(z with a signal component in t ); through the hypothesis testing model in the signal detection theory, the detector T(z t ); and pass the false alarm probability P FA get the detector T(z t ) threshold γ t ; when the detector T(z t ) is less than a given threshold γ t , that is, T(z t )≤γ t , then it can be judged that the remaining vector does not contain signal components, the algorithm stops iterating, and the recovery value of the signal is obtained; the following is the adaptive compressed sensing signal recovery method based on the greedy algorithm proposed in the present invention, called Detection-based Orthogonal Matching Pursuit, DOMP, the flow of the algorithm scheme is as follows figure 2 shown, the specific steps are as fol...