Abstract
Linear underdetermined blind source separation (UBSS) is a useful but difficult problem for its illness settings. In this paper, based on compressed sensing (CS) theory, the model inherent connections between UBSS and CS is analysed on the basis of sparsity of the source signals. The mathematical model of underdetermined blind source recovery by CS is built. In order to build the estimated measurement matrix, the source number and mixing matrix are estimated using the refined clustering procedure based on unsupervised robust C prototypes (URCP) method, the measurement matrix and the measurement equation are obtained according to the proposed combined underdetermined blind source recovery model. Then, the proposed blind compressed recovery (BCR) algorithm is derived based on the signal sparse compressive sampling matching pursuit (SSCoSaMP) scheme, which realizes the reconstruction of the underdetermined sparse source signals efficiently. Simulations are provided to show the effectiveness of the proposed method using artificial data.
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Wang, F., Li, R., Wang, Z., Li, D. (2015). Improved Blind Recovery Algorithm for Underdetermined Mixtures by Compressed Sensing. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_44
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DOI: https://doi.org/10.1007/978-3-319-20472-7_44
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