Blind signal separation of rational functions using Löwner-based tensorization

A novel deterministic blind signal separation technique for separating signals into rational functions is proposed, applicable in various situations. This new technique is based on a tensorization of the observed data matrix into a set of Löwner matrices. The obtained tensor can then be decomposed with a block tensor decomposition, resulting in a unique separation into rational functions under mild conditions. This approach provides a viable alternative to independent component analysis (ICA) in cases where the independence assumption is not valid or where the sources can be modeled well by rational functions, such as frequency spectra. In contrast to ICA, this technique is deterministic and not based on statistics, and therefore works well even with a small number of samples.