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Sparse signal reconstruction by swarm intelligence algorithms
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DOI：10.1016/j.jestch.2020.09.006.png)
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摘要
En 中文
This study introduces a new technique for sparse signal reconstruction. In general, there are two classes of algorithms in the recovery of sparse signals: greedy approaches and l(1)-minimization methods. The proposed method employs swarm intelligence based techniques for sparse signal reconstruction. With this technique, the proposed method tries to find nonzero entries of a sparse signal. In addition, it uses least square method to obtain the magnitude of the reconstructed signal. In this study, artificial bee colony and particle swarm optimization algorithms are employed for the reconstruction of sparse signals. The algorithms are tested on some benchmark problems and empirical results for a number of test cases are obtained. In addition, the reconstruction performances of these two algorithms are compared with l(1) minimization, greedy algorithms and several lately announced methods. According to the results of the tests and comparisons, the proposed method for artificial bee colony and particle swarm optimization algorithms have better performance than the classical methods for some test cases and it can be used to recover sparse signals in general. Additionally, this method can be used to recover a sparse signal when classical methods fail to reconstruct the signal for some cases. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.
Keyword:
Compressive sensing
Sparse signal reconstruction
Swarm intelligence algorithms
Artificial bee colony algorithm
Particle swarm optimization
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5.4
论文数:
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被引数:
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