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Variable step-size matching pursuit based on oblique projection for compressed sensing

机译：基于倾斜投影的可变梯级匹配追踪压缩检测

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摘要

The development of compressive sensing has focused on sparse signal reconstruction in recent years. Most existing greedy algorithms achieve satisfactory reconstruction performance only when the sparsity of the target signal has been known as prior information. Moreover, some greedy algorithms always involve either high-computational expenses or low-reconstruction accuracy caused by the process of adaptive adjustment of signal sparsity. To address these concerns, a novel variable step-size matching pursuit based on oblique projection (VSMPOP) for compressed sensing is proposed. The proposed VSMPOP algorithm estimates the initial sparsity based on the restricted isometry property criterion. The algorithm creates a support set of the target signal after a preliminary test and oblique projection test between the sensing matrix and the residual. VSMPOP realises a similar approach to the sparsity level with a variable step size. The experimental results demonstrated that the proposed VSMPOP algorithm provides superior performance in terms of computational complexity and reconstruction efficiency compared with most of the available matching pursuit algorithms.
机译：近年来，压缩传感的发展集中于稀疏信号重建。只有当目标信号的稀疏性被称为先前信息时，大多数现有的贪婪算法才能实现令人满意的重建性能。此外，一些贪婪的算法始终涉及由信号稀疏的自适应调整过程引起的高计算费用或低重构准确性。为了解决这些问题，提出了一种基于倾斜投影（VSMPOP）来压缩感测的新型可变步长匹配追求。所提出的VSMPOP算法估计基于受限制的等距属性标准的初始稀疏性。该算法在感测矩阵和残差之间的初步测试和倾斜投影测试之后创建一个目标信号的支持集。 VSMPOP实现了具有变量步长的稀疏度水平的类似方法。实验结果表明，与大多数可用匹配追踪算法相比，该求购算法在计算复杂性和重建效率方面提供了卓越的性能。

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