Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6957259 | Signal Processing | 2018 | 32 Pages |
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 nââ1 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 nââ1 minimization. The analysis of the derived bounds reveals that weighted nââ1 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, â1-â1 minimization, Modified-CS, regularized Modified-CS, and weighted â1 minimization-in terms of both the theoretical bounds and the practical performance.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Huynh Van Luong, Nikos Deligiannis, Jürgen Seiler, Søren Forchhammer, André Kaup,