@inproceedings{d94d199ca8214766b923409b679ff2f1,
title = "Blind source separation based on compressed sensing",
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.",
keywords = "Blind Source Separation, Compressed Sensing, FOOMP, RIP, Redundant Dictionary, Sparsity",
author = "Zhenghua Wu and Yi Shen and Qiang Wang and Jie Liu and Bo Li",
year = "2011",
doi = "10.1109/ChinaCom.2011.6158262",
language = "英语",
isbn = "9781457701016",
series = "Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011",
pages = "794--798",
booktitle = "Proceedings of the 2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011",
note = "2011 6th International ICST Conference on Communications and Networking in China, CHINACOM 2011 ; Conference date: 17-08-2011 Through 19-08-2011",
}