A data-derived quadratic independence measure for adaptive blind source recovery in practical applications

We present a novel performance index to measure the statistical independence of data sequences and apply it to the framework of blind source recovery (BSR) that includes blind source separation, deconvolution and equalization. This performance index is capable of measuring the mutual independence of data sequences directly from the data. This information theoretic; quadratic independence measure (QIM) is derived based on Renyi's quadratic entropy estimated by a finite data length Parzen window using a Gaussian kernel. Simulation results are presented to validate the performance of the proposed benchmark and compare it with other established benchmarks.