Greedy algorithm-based self-adapting compression perception signal restoring method

A compressed sensing and signal recovery technology, applied in the field of signal processing, which can solve problems that are difficult to directly apply to unknown sparsity, unknown signal sparsity, and inappropriate computational convex relaxation methods.

CN104485965AInactive Publication Date: 2015-04-01UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2015-04-01
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a greedy algorithm-based self-adapting compression perception signal restoring method. On the basis of an OMP (Orthogonal Matching Pursuit) algorithm, a detector used for judging whether a signal component exists in residual vector or not is designed, the detector is obtained through a hypothesis test model in the signal detection theory, the threshold of the detector is given through preset false alarm probability, when the threshold of the detector is smaller than the given threshold, the state that the signal component does not exist in residual vector can be judged, at the moment, iteration is stopped in the algorithm and restoring value of the signal is obtained, otherwise, iteration is continued. According to the method, the greedy algorithm serves as a channel estimation method without depending on the multipath number of a channel; compared with the MDL (Minimum Description Length), the method has the advantage that communication resources are saved due to the fact that observation for multiple times is not required; the method has the advantage of smaller calculation amount of the greedy algorithm, the problem that the requirement of the greedy algorithm on the real-time capability of sparse signal restoration is relatively high is solved, and the method can be applied to the condition that the signal sparsity is unknown.
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Description

technical field

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

[0002] Compressed sensing, a signal processing method for dealing with sparse vectors, is one of the most important theories proposed in this century. Compressive sensing technology can restore sparse signals from fewer sample points than the Nyquist sampling rate, that is, y=Ax+e, where, is the observation vector, through the observation matrix For a k-sparse signal Observation, where the dimension of the observation vector y is much smaller than the dimension of the signal x, m<n, 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 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 present invention is: the adaptive compressed sensing signal recovery method based on the greedy algorithm is based on the OMP algorithm, and a judgment residual vector r is designed t Is there still a detector T(z with signal component in t ); Through the hypothesis testing model in signal detection theory, the detector T(z t ); and through 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 , namely T(z t )≤γ t , then it can be judged that there is no signal component in the remaining vector, then the algorithm stops iterating and obtains the recovery value of the signal; the following is the adaptive compressed sensing signal recovery method based on the greedy algorithm proposed by the present invention, which is called Detection-based Orthogonal Matching Pursuit, DOMP, the process of the algorithm scheme is as follows figure 2 As shown, the specific steps a...