利用奇异值分解的鲁棒盲信源分离

Abstract

Abstract:

From the point of view of the estimating function proposed by Amari, we investigate the intrinsic characteristics of the optimal separating matrix for blind source separation (BSS) in the sense of minimizing nonlinear principal component analysis criterion, which formulates the optimal solution as the normalized cross correlation between the input and nonlinearized output. We thus present a singular value decomposition (SVD) based robust scheme for BSS, which considers the estimating procedure of the separating matrix as a nonlinear power iteration problem. By performing SVD of the power term, the computational load can be significantly reduced, which results from circumventing the difficulty of solving the inverse square root of the normalization term. Since the separating matrix is properly normalized by the positive definite inverse square root of the power term, the robustness of this algorithm is greatly improved. Moreover, this guarantees the orthonormality of the separating matrix at each iteration. Some simulation results are also provided to demonstrate the performance of the proposed algorithm.

Key words: blind source separation (BSS), singular value decomposition (SVD), nonlinear power iteration (NPI), pre-whitening

CLC Number:

TN911.7

ZHANG Wei-tao;LOU Shun-tian;ZHANG Yan-liang. SVD based robust approach for blind source separation[J].J4, 2009, 36(6): 985-989.

References

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SVD based robust approach for blind source separation

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