Source separation and tracking for time-varying systems

Abstract

Many situations arise when it is desired to recover from a noisy mixture of data both an estimate of a propagating source signal as well as its angular position. Accordingly, the problem of source separation and tracking for time-varying systems of moving sources is studied. Using techniques borrowed from independent component analysis (ICA) and numerical linear algebra, a processing solution is formulated. The ULV decomposition (ULVD), a member of the complete orthogonal decompositions (CODs) family is used to normalize and decorrelate the data. Since the ULVD is known to stably update and downdate with time-varying data, this step is named ULVD adaptive whitening. The ICA-based EASI (equivariant adaptive separation based on independence) algorithm is adapted to provide not only separated sources but direction-of-arrival (DOA) estimates. Developed improvements to this technique are an adaptive filter method for estimating the mixing matrix from the newly estimated signals as well as a simple cross-over detection scheme. Finally, the developed solutions are tested in simulation examples using meaningful performance measures.