Sparse signal recovery with multiple prior information: algorithm and measurement bounds
This publication appears in: Signal Processing
Authors: H. Van Luong, N. Deligiannis, J. Seiler, S. Forchhammer and A. Kaup
Publication Year: 2018
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 themultiple 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 classicalcompressed sensing (CS). We evaluate experimentally the proposed RAMSI algorithm and the establishedbounds using numerical sparse signals. The results show that the proposed algorithm outperforms stateof-the-artalgorithmsincluding classical CS, 1ǃ minimization, Modified-CS, regularized Modified-CS,and weighted 1 minimizationin terms of both the theoretical bounds and the practical performance.