TY - GEN
T1 - On theoretical optimization of the sensing matrix for sparse-dictionary signal recovery
AU - Zhu, Jianchen
AU - Zhao, Shengjie
AU - Ma, Xu
AU - Arce, Gonzalo R.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. Here, we first propose the Signal space Subspace Pursuit (SSSP) algorithm, and then we derive a low bound on the number of measurements required. The algorithm has low computational complexity and provides high recovery accuracy.
AB - Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. Here, we first propose the Signal space Subspace Pursuit (SSSP) algorithm, and then we derive a low bound on the number of measurements required. The algorithm has low computational complexity and provides high recovery accuracy.
KW - D-restricted isometry isometry property (D-RIP)
KW - Overcomplete dictionary
KW - Signal space greedy method
KW - Sparse representation
UR - https://www.scopus.com/pages/publications/85079268868
U2 - 10.1109/GlobalSIP45357.2019.8969228
DO - 10.1109/GlobalSIP45357.2019.8969228
M3 - Conference contribution
AN - SCOPUS:85079268868
T3 - GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
BT - GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
Y2 - 11 November 2019 through 14 November 2019
ER -