Compressive blind mixing matrix recovery algorithm based on gradient ascent

Compressed Sensing (CS) shows that, when signal is sparse or compressible with respect to some basis, only a small number of compressive measurements of original signal can be sufficient for exact (or approximate) recovery. While in some cases, only the mixtures of original sources are available for observation without knowing the priori information of both the source signals and the mixing process. To recover the original sources, estimating the mixing process is a key step. In this paper, we estimate the mixing matrix in the compressive measurement domain based on gradient ascent. The innovation lies in that, we recover the mixing matrix directly from the observed compressive measurements of mixture signals, without recovering the mixture signals at first. Numerical experiments show that the mixing matrix can be well estimated via the proposed method.