Complex Valued Robust Multidimensional SOBI

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)


Complex valued random variables and time series are common in various applications, for example in wireless communications, radar applications and magnetic resonance imaging. These applications often involve the famous blind source separation problem. However, the observations rarely fully follow specific models and robust methods that allow deviations from the model assumptions and endure outliers are required. We propose a new algorithm, robust multidimensional eSAM-SOBI, for complex valued blind source separation. The algorithm takes into account possible multidimensional spatial or temporal dependencies, whereas traditional SOBI-like procedures only consider dependencies in a single direction. In applications like functional magnetic resonance imaging, the dependencies are indeed not only one-dimensional. We provide a simulation study with complex valued data to illustrate the better performance of the methods that utilize multidimensional autocovariance in the presence of two-dimensional dependency. Moreover, we also examine the performance of the multidimensional eSAM-SOBI in the presence of outliers.


Complex valued BSS SOBI Time series Multidimensional autocovariance 



The work of Klaus Nordhausen was supported by the Academy of Finland Grant 268703. The authors wish to thank the referees for their excellent comments that helped improve the paper.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Mathematics and Systems AnalysisAalto University School of ScienceAaltoFinland
  2. 2.Department of Mathematics and StatisticsUniversity of TurkuTurkuFinland
  3. 3.School of Health SciencesUniversity of TampereTampereFinland

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