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首页> 外文期刊>Circuits, systems, and signal processing >Adaptive Step-Size Matching Pursuit Algorithm for Practical Sparse Reconstruction
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Adaptive Step-Size Matching Pursuit Algorithm for Practical Sparse Reconstruction

机译：实用的稀疏重建自适应步长匹配追踪算法

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摘要

A novel adaptive step-size matching pursuit algorithm (AStMP) is proposed. AStMP reduces the computational cost and increases the accuracy of reconstructing practical signals (i) whose sparsity K is unknown and/or (ii) may be corrupted by noise. AStMP accurately estimates K by combining the sparsity estimate of sparsity adaptive subspace pursuit (SASP) with the adaptively changing stepsize at each stage of sparsity adaptive matching pursuit (SAMP). Thus, AStMP can quickly achieve accurate estimation of the sparsity level and the true support set of the target signals. Meanwhile, a preselection is employed to reduce the computational complexity of each stage. When K is greater than half the number of measurements M, the probability of exact recovery is improved by analyzing the support set. Since the stepsize changes adaptively, AStMP can be applied in both the noiseless and noisy cases when the signal is not strictly sparse, allowing for exact or approximate signal recovery. Experimental results demonstrate that the AStMP is effective in fast and exact reconstruction and has good performance.
机译：提出了一种新的自适应步长匹配跟踪算法（AStMP）。 AStMP降低了计算成本，并提高了重建实际信号的准确性（i）稀疏度K未知，和/或（ii）可能被噪声破坏。 AStMP通过将稀疏自适应子空间追踪（SASP）的稀疏度估计与稀疏自适应匹配追踪（SAMP）的每个阶段的自适应变化步长进行组合来准确估算K。因此，ASTmP可以快速实现对稀疏度水平和目标信号真实支持集的准确估计。同时，采用预选择来减少每个阶段的计算复杂度。当K大于测量值M的一半时，通过分析支持集可以提高准确恢复的可能性。由于步长大小会自适应地变化，因此，在信号并非严格稀疏的情况下，ASTmP可以应用于无噪声和嘈杂的情况，从而实现精确或近似的信号恢复。实验结果表明，ASTMP可以快速，准确地重建图像并具有良好的性能。

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