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Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing 210044, China
School of Instrumental Science and Engineering, Southeast University, 2 Sipailou, Nanjing, 210096, China
School of Information Science and Engineering, Yunnan University, No. 2 North Green Lake Road, Kunming 650091, China
* Author to whom correspondence should be addressed.
Received: 16 November 2013; in revised form: 11 December 2013 / Accepted: 11 December 2013 / Published: 17 December 2013
Abstract: This paper designs and evaluates a variant of CoSaMP algorithm, for recovering the sparse signal s from the compressive measurement given a fixed low-rank subspace spanned by U. Instead of firstly recovering the full vector then separating the sparse part from the structured dense part, the proposed algorithm directly works on the compressive measurement to do the separation. We investigate the performance of the algorithm on both simulated data and video compressive sensing. The results show that for a fixed low-rank subspace and truly sparse signal the proposed algorithm could successfully recover the signal only from a few compressive sensing (CS) measurements, and it performs better than ordinary CoSaMP when the sparse signal is corrupted by additional Gaussian noise.
Keywords: compressive sensing; sparse signal recovery; greedy algorithm; video surveillance
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MDPI and ACS Style
He, J.; Gao, M.-W.; Zhang, L.; Wu, H. Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement. Algorithms 2013, 6, 871-882.
He J, Gao M-W, Zhang L, Wu H. Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement. Algorithms. 2013; 6(4):871-882.
He, Jun; Gao, Ming-Wei; Zhang, Lei; Wu, Hao. 2013. "Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement." Algorithms 6, no. 4: 871-882.