Abstract
A recovery algorithm is one of the most important components in compressive sensing. It is responsible for the recovery of sparse coefficients in some bases of the original signal from a set of non-adaptive and underdetermined linear measurements, and it is a key link between the front-end signal sensing system and back-end processing. In this study, an improved orthogonal matching pursuit algorithm based on singular value decomposition is proposed to overcome the limitations of existing algorithms, which effectively eliminates the correlation between the measured values. The results of simulation experiments show that the proposed algorithm significantly improves the average signal-to-noise ratio, and it performs more robustly than the classical orthogonal matching pursuit algorithm.
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