Separation Of Sources Using Higher-Order Cumulants

Pierre Comon

doi:10.1117/12.962275

14 November 1989 Separation Of Sources Using Higher-Order Cumulants

Pierre Comon

Proceedings Volume 1152, Advanced Algorithms and Architectures for Signal Processing IV; (1989) https://doi.org/10.1117/12.962275
Event: 33rd Annual Technical Symposium, 1989, San Diego, United States

Abstract

The problem is to recover stochastic processes from an unknown stationary linear transform. Our contribution is two-fold. First we focus on instantaneous mixtures: observation e(t) is assumed to write as a regular linear transform of the sources, x(t), as e(t)=Box(t). The only assumption requested is that the sources xi(t) are mutually independent, and no additional knowledge upon their statistics is necessary provided they are not normal. Extensions to convolutional mixing are then pointed out, namely cases where e(t)=A(t)*x(t) where A(t) has a rational transfer function. Sensitive improvements to the algorithm of Giannakis et al for MA identification are included. Multivariate ARMA identification can be split into three successive estimation problems: AR identification, monic MA identification, and estimation of B_o in last position.

Citation Download Citation

Pierre Comon "Separation Of Sources Using Higher-Order Cumulants", Proc. SPIE 1152, Advanced Algorithms and Architectures for Signal Processing IV, (14 November 1989); https://doi.org/10.1117/12.962275

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