Li, Xinyi; Liu, Sanyang; Zhang, Zhaohui Adaptive reweighting via GPSR algorithm for compressed sensing signal reconstruction. (Chinese. English summary) Zbl 1413.94023 J. Zhejiang Univ., Sci. Ed. 45, No. 2, 156-161 (2018). Summary: In order to improve the performance of gradient projection for sparse reconstruction (GPSR) algorithm effectively during the process of compressed sensing signal reconstruction, the weight coefficient for penalty is introduced into the reconstruction model. Its advantage is to find the best balance between the complexity and the construction precision. The weights are adaptively adjusted during every iterative step to accelerate the convergence. Simulation experiment results show that the proposed algorithm has a better performance on computational efficiency than that of the traditional GPSR algorithm and the typical OMP algorithm, enabling high precision reconstruction in less time. MSC: 94A12 Signal theory (characterization, reconstruction, filtering, etc.) Keywords:compressed sensing; signal reconstruction; GPSR algorithm; adaptive ideal PDFBibTeX XMLCite \textit{X. Li} et al., J. Zhejiang Univ., Sci. Ed. 45, No. 2, 156--161 (2018; Zbl 1413.94023) Full Text: DOI