Fast Thresholding Algorithms with Feedbacks and Partially Known Support for Compressed Sensing

Kaiyan Cui (), Zhanjie Song and Ningning Han ()
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Kaiyan Cui: School of Mathematics, Tianjin University, Tianjin 300350, P. R. China
Zhanjie Song: School of Mathematics, Tianjin University, Tianjin 300350, P. R. China2Visual Pattern Analysis Research Lab, Tianjin University, Tianjin 300072, P. R. China
Ningning Han: School of Mathematics, Tianjin University, Tianjin 300350, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2020, vol. 37, issue 03, 1-20

Abstract: Some works in modified compressive sensing (CS) show that reconstruction of sparse signals can obtain better results than traditional CS using the partially known support. In this paper, we extend the idea of these works to the null space tuning algorithm with hard thresholding, feedbacks (NST + HT + FB) and derive sufficient conditions for robust sparse signal recovery. The theoretical analysis shows that including prior information of partially known support relaxes the preconditioned restricted isometry property condition comparing with the NST + HT + FB. Numerical experiments demonstrate that the modification improves the performance of the NST+HT+FB, thereby requiring fewer samples to obtain an approximate reconstruction. Meanwhile, a systemic comparison with different methods based on partially known support is shown.

Keywords: Compressed sensing; fast thresholding algorithm; signal reconstruction; sparse approximation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S021759592050013X

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