Adaptive compressed sensing using sparse measurement matrices

Abstract

Compressed sensing methods using sparse measure- ment matrices and iterative message-passing recovery procedures are recently investigated due to their low computational complex- ity and excellent performance. The design and analysis of this class of methods is inspired by a large volume of work on sparse- graph codes such as Low-Density Parity-Check (LDPC) codes and the iterative Belief-Propagation (BP) decoding algorithms. In par- ticular, we focus on a class of compressed sensing methods emerg- ing from the Sudocodes scheme that follow similar ideas used in a class of sparse-graph codes called rateless codes. We are inter- ested in the design and analysis of adaptive Sudocodes methods and this paper provides initial steps in this direction.

Publication
International 2014 Traveling Workshop on Interactions between Sparse models and Technology, Proceedings