TO THE IEEE TRANSACTIONS ON SIGNAL PROCESSING , October
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چکیده
| Source separation consists in recovering a set of independent signals when only mixtures with unknown coeecients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Equivariant Adaptive Separation via Independence). The EASI algorithms are based on the idea of serial updating: this speciic form of matrix updates systematically yields algorithms with a simple, parallelizable structure, for both real and complex mixtures. Most importantly , the performance of an EASI algorithm does not depend on the mixing matrix. In particular, convergence rates, stability conditions and interference rejection levels depend only on the (normalized) distributions of the source signals. Close form expressions of these quantities are given via an asymptotic performance analysis. This is completed by some numerical experiments illustrating the eeectiveness of the proposed approach.
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تاریخ انتشار 1996