Sparse Signal Recovery with Multiple Prior Information: Algorithm and Measurement Bounds

van Luong H, Deligiannis N, Seiler J, Forchhammer S, Kaup A (2018)


Publication Language: English

Publication Type: Journal article

Publication year: 2018

Journal

Book Volume: 152

Pages Range: 417-428

URI: https://www.sciencedirect.com/science/article/pii/S0165168418302160

DOI: 10.1016/j.sigpro.2018.06.019

Abstract

We address the problem of reconstructing a sparse signal from compressive measurements with the aid of multiple known correlated signals. We propose a reconstruction algorithm with multiple side information signals (RAMSI), which solves an n − l1 minimization problem by weighting adaptively the multiple side information signals at every iteration. In addition, we establish theoretical bounds on the number of measurements required to guarantee successful reconstruction of the sparse signal via weighted n − l1 minimization. The analysis of the derived bounds reveals that weighted n − l1 minimization can achieve sharper bounds and significant performance improvements compared to classical compressed sensing (CS). We evaluate experimentally the proposed RAMSI algorithm and the established bounds using numerical sparse signals. The results show that the proposed algorithm outperforms state-of-the-art algorithms—including classical CS, l1-l1 minimization, Modified-CS, regularized Modified-CS, and weighted l1 minimization—in terms of both the theoretical bounds and the practical performance.

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How to cite

APA:

van Luong, H., Deligiannis, N., Seiler, J., Forchhammer, S., & Kaup, A. (2018). Sparse Signal Recovery with Multiple Prior Information: Algorithm and Measurement Bounds. Signal Processing, 152, 417-428. https://doi.org/10.1016/j.sigpro.2018.06.019

MLA:

van Luong, Huynh, et al. "Sparse Signal Recovery with Multiple Prior Information: Algorithm and Measurement Bounds." Signal Processing 152 (2018): 417-428.

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