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An improved blind Gaussian source separation approach based on generalized Jaccard similarity

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Abstract

Blind source separation (BSS) consists of recovering the independent source signals from their linear mixtures with unknown mixing channel. The existing BSS approaches rely on the fundamental assumption: the number of Gaussian source signals is no more than one, this limited the use of BSS seriously. To overcome this problem and the weakness of cosine index in measuring the dynamic similarity of signals, this study proposes the fuzzy statistical behavior of local extremum based on generalized Jaccard similarity as the measure of signal’s similarity to implement the separation of source signals. In particular, the imperialist competition algorithm is introduced to minimize the cost function which jointly considers the stationarity factor describing the dynamical similarity of each source signal separately and the independency factor describing the dynamical similarity between source signals. Simulation experiments on synthetic nonlinear chaotic Gaussian data and ECG signals verify the effectiveness of the improved BSS approach and the relatively small cross-talking error and root mean square error indicate that the approach improves the accuracy of signal separation.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant 61573014 and Natural Science Basic Research Program of Shaanxi (Program No. 2024JC-YBMS-043).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    Xudan Fu & Jimin Ye

  2. School of Mathematics and Physics, Guangxi Minzu University, Nanning, 530006, China

    Jianwei E

Authors
  1. Xudan Fu
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  2. Jimin Ye
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  3. Jianwei E
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Contributions

Fu Xudan, Ye Jimin, and Jianwei E carried out the blind source separation of Gaussian signals based on generalized Jaccard similarity. Among them, Fu xudan put forward the idea of the research, participated in its design and coordination and drafted the manuscript, Ye Jimin participated in the construction of the framework of the paper, and helped to draft the manuscript, Jianwei E participated in the design of the experiment and helped to draft the manuscript. All authors reviewed the manuscript and approved the final draft.

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Correspondence to Jimin Ye.

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Fu, X., Ye, J. & E, J. An improved blind Gaussian source separation approach based on generalized Jaccard similarity. Analog Integr Circ Sig Process 119, 363–373 (2024). https://doi.org/10.1007/s10470-024-02264-1

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  • DOI: https://doi.org/10.1007/s10470-024-02264-1

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