GLRT for testing separability of a complex-valued mixture based on the Strong Uncorrelating Transform - Ramirez, D.; Schreier, P. J.; Via, J.; Santamaria, I. (2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012)
The Strong Uncorrelating Transform (SUT) allows blind separation of a mixture of complex independent sources if and only if all sources have distinct circularity coefficients. In practice, the circularity coefficients need to be estimated from observed data. We propose a generalized likelihood ratio test (GLRT) for separability of a complex mixture using the SUT, based on estimated circularity coefficients. For distinct circularity coefficients (separable case), the maximum likelihood (ML) estimates, required for the GLRT, are straightforward. However, for circularity coefficients with multiplicity larger than one (non-separable case), the ML estimates are much more difficult to find. Numerical simulations show the good performance of the proposed detector.