An enhanced block-based Compressed Sensing technique using orthogonal matching pursuit

Das, Sujit; Mandal, Jyotsna Kumar

doi:10.1007/s11760-020-01777-2

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…The StOMP algorithm is a greedy algorithm improved from the OMP algorithm. 20 The greedy algorithm selects the 2K or K atoms, whose inner product is the largest in the iterative process. The StOMP algorithm determines the atoms through a threshold, and the input parameters do not include the signal sparsity K. Compared with the ROMP algorithm, the StOMP algorithm has higher computational efficiency and reconstructed signal accuracy.…”

Section: Segment Omp Algorithm Improvementmentioning

confidence: 99%

An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks

Wu³

et al. 2022

International Journal of Distributed Sensor Networks

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Aiming at the problems of low data reconstruction accuracy in wireless sensor networks and users unable to receive accurate original signals, improvements are made on the basis of the stagewise orthogonal matching pursuit algorithm, combined with sparseness adaptation and the pre-selection strategy, which proposes a sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm. In the framework of the stagewise orthogonal matching pursuit algorithm, the algorithm in this article uses a combination of a fixed-value strategy and a threshold strategy to screen the candidate atom sets in two rounds to improve the accuracy of atom selection, and then according to the sparsity adaptive principle, the sparse approximation and accurate signal reconstruction are realized by the variable step size method. The simulation results show that the algorithm proposed in this article is compared with the orthogonal matching pursuit algorithm, regularized orthogonal matching pursuit algorithm, and stagewise orthogonal matching pursuit algorithm. When the sparsity is 35 < K < 45, regardless of the size of the perception matrix and the length of the signal, M = 128, N = 256 or M = 128, N = 512 are improved, and the reconstruction time is when the sparsity is 10, the fastest time between 25 and 25, that is, less than 4.5 s. It can be seen that the sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm has better adaptive characteristics to the sparsity of the signal, which is beneficial for users to receive more accurate original signals.

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Section: Segment Omp Algorithm Improvementmentioning

confidence: 99%