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Research on Improvement of Stagewise Weak Orthogonal Matching Pursuit Algorithm

机译:平稳弱正交匹配追踪算法改进研究

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摘要

One of the key technologies of compressed sensing is the signal reconstruction. And the two important indicators of signal reconstruction are the reconstruction probability and the time consumed. The Stagewise Weak Orthogonal Matching Pursuit (SWOMP) is widely used because the sparsity does not need to be a priori condition. The use of fixed threshold parameter in the iterative process can easily lead to overestimation and underestimation. Inspired by the idea of “the initial stage is approaching quickly and the final stage is approaching gradually,” that is, the search rule of “firstly fast and then slow,” an improved algorithm replacing the fixed threshold selection with S-shaped function value in each iteration is proposed to overcome the shortcoming that the fixed threshold parameter is selected in every iteration of SWOMP algorithm. Through compared experiment of six different S-shaped functions, the results show that the influence of different S-shaped functions on the SWOMP algorithm is different, and the improved SWOMP algorithm with the sixth S-shaped function has the best reconstruction effect.
机译:压缩感测的关键技术之一是信号重建。信号重建的两个重要指标是重建概率和消耗的时间。平面使用突破性弱正交匹配追踪(SWOMP),因为稀疏不需要是先验状态。在迭代过程中使用固定阈值参数可以很容易地导致高估和低估。灵感灵感来自“初始阶段快速接近,最后阶段逐步接近”,即“首先快速然后慢,”的改进算法用S形函数值替换固定阈值选择的搜索规则在每次迭代中,提出克服在SWOMP算法的每次迭代中选择固定阈值参数的缺点。通过比较六种不同的S形函数的实验,结果表明,不同的S形功能对SWOMP算法的影响是不同的,并且具有第六个函数的改进的SWOMP算法具有最佳的重建效果。

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