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Blind Separation of Noncircular Sources Via Approximate Joint Diagonalization of Augmented Charrelation Matrices

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Abstract

An augmented charrelation matrix (ACM), which can utilize both the conventional and the conjugate statistical information in the complex domain, is proposed. The ACM additionally makes use of the conjugate Hessian matrix (namely conjugate charrelation matrix) of the observations of noncircular sources. A blind separation scheme built on the approximate joint diagonalization (AJD) principle is introduced, which precedes some numerical examples to demonstrate the improved performance of the ACM-AJD approach compared with some algorithms in the literature.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61072098, 61072099, 61331019).

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Xiaoming Gou, Zhiwen Liu, Jingyan Ma & Yougen Xu

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  1. Xiaoming Gou
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  2. Zhiwen Liu
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  3. Jingyan Ma
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  4. Yougen Xu
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Correspondence to Yougen Xu.

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Gou, X., Liu, Z., Ma, J. et al. Blind Separation of Noncircular Sources Via Approximate Joint Diagonalization of Augmented Charrelation Matrices. Circuits Syst Signal Process 34, 695–705 (2015). https://doi.org/10.1007/s00034-014-9867-5

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  • DOI: https://doi.org/10.1007/s00034-014-9867-5

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