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

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.

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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Problems solved by technology

[0016] In practical applications, when the problem dealt with by compressive sensing technology requires high signal processing speed, it is generally inappropriate to use the convex relaxation method with a large amount of calculation.

In addition, in the actual signal processing problem, usually the sparsity of the signal is unknown, therefore, the greedy algorithm cannot be directly used to restore the sparse signal

Based on the dependence on sparsity, it is difficult for the greedy algorithm to be directly applied to problems with unknown sparsity in compressed sensing technology.

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