Adaptive Estimation of the Strong Uncorrelating Transform with Applications to Subspace Tracking

In some signal processing tasks involving complex-valued multichannel measurements, classical whitening approaches do not completely remove the second-order statistical dependencies of the data. This paper describes adaptive procedures for estimating the strong uncorrelating transform for jointly diagonalizing the covariance and pseudo-covariance matrices of multidimensional signals. Novel algorithms are derived that extend and combine the power method and orthogonal iterations with ordinary fixed and iterative whitening procedures. Finally, we show how to combine our procedures with orthogonal PAST algorithms to perform subspace tracking and source signal clustering based on non-circularity