【摘要】 研究压缩感知信号重构算法,提出了一种不需要精确知道信号稀疏度的先验知识,就能重构出目标信号的盲稀疏度迭代贪婪跟踪重构新算法.采用分段的方法来逐段估计、扩充目标信号的真实支撑域,并应用后向追踪思想,自适应地调整候选序列,以便每一次迭代时更加精确地估计真正的支撑域.理论分析与实验证明,算法性能超过了现有的迭代贪婪跟踪重构算法性能;给出了迭代贪婪跟踪信号重构的统一框架,正交匹配跟踪和子空间跟踪算法可以看成它的特例;在计算复杂度和重构算法性能之间做出了最佳折衷;有更强的实用性.更多还原

【Abstract】 A new blind sparsity iterative greedy reconstruction algorithm is presented based on studying the signal reconstruction algorithm for compressed sensing without the prior information of signal sparsity.A stage-wised and backtracking method is employed to adaptively adjust the candidate list at each iteration in order to estimate the true supporting set of the approximated signal.The theoretical analysis and experiment simulation prove that the performance of the algorithm outperforms that of the existing state-of-art iterative greedy matching pursuit algorithms,and provides a generalized greedy reconstruction framework.The orthogonal matching pursuit and subspace pursuit can be viewed as its special case,and it also gives the best trade-offs between computational complexity and reconstruction performance.This makes it a promising candidate for many practical applications for compressed sensing signal reconstruction.更多还原