Research Article
Blind Source Separation Based On Compressed Sensing
@INPROCEEDINGS{10.1109/ChinaCom.2011.6158262, author={Zhenghua Wu and Yi Shen and Qiang Wang and Jie Liu and Bo Li}, title={Blind Source Separation Based On Compressed Sensing}, proceedings={6th International ICST Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2012}, month={3}, keywords={blind source separation compressed sensing foomp rip redundant dictionary sparsity}, doi={10.1109/ChinaCom.2011.6158262} }- Zhenghua Wu
Yi Shen
Qiang Wang
Jie Liu
Bo Li
Year: 2012
Blind Source Separation Based On Compressed Sensing
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2011.6158262
Abstract
Blind Source Separation (BSS) is an important issue in the coherent processing of multi-dimensional data. To recover and separate the sources from underdetermined mixtures, some prior information like sparse representation is required. The principle is very similar to the new technique named Compressed Sensing (CS), which asserts that one can recover a sparse signal from a limited number of random projections. In this paper, the relationship between BSS and CS is studied by equivalent transformation, then we propose the linear operator by which the relationship between the sources and the mixtures is modeled in two ways: RIP and incoherence, and give some instructive conclusions for the operator design. Numerical simulation applying the FOOMP algorithm and a operator we propose are conducted to demonstrate the good performance of the whole framework.