Exploiting Signal Subspace Invariance to Resolve Non-Stationary, Non-Orthogonal Sensor Array Signals in Correlated Noise Fields

We investigate a new approach for the problem of source separation/classification of non-orthogonal, non-stationary noisy signals impinging on an array of sensors. We propose a solution to the problem when the contaminating noise is temporally and spatially correlated. The observations are projected onto a nested set of multiresolution spaces prior to classical eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the level of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the correlated signal sources in the context of ´best basis´ selection. The technique shows robustness to source non-stationarity as well as anisotropic properties of the channel characteristics under no constraints on the array design. We illustrate the high performance of the technique on simulated and experimental multichannel neurophysiological data measurements