van Luong H, Deligiannis N, Seiler J, Forchhammer S, Kaup A (2018)
Publication Language: English
Publication Type: Journal article
Publication year: 2018
Book Volume: 152
Pages Range: 417-428
URI: https://www.sciencedirect.com/science/article/pii/S0165168418302160
DOI: 10.1016/j.sigpro.2018.06.019
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.
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://dx.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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