Semi-parametric statistical approach for overdetermined blind source separation
Jin Hai-hong · Chinese Journal of Radio Science · 2006
This paper addresses the problem of overdetermined blind source separation (ODBSS). Firstly, it is shown there exits, in the sense of essential equality, a unique m×m nonsingular de-mixing matrix, where the outputs of the separation system consist of the scaled and permuted source signals plus zero signals. Secondly, based on the semiparametric theory, an estimating function is constructed and the corresponding learning algorithms are proposed. It is proved that the proposed algorithms for ODBSS is equivariant and has the property of keeping the demixing matrix from becoming singular. Due to the uniqueness of equilibrium or separating point of the algorithms, the new algorithms converge stably. The validity and stability of the new algorithms are illustrated via the computer simulations on ODBSS with an unknown and at the same time dynamic changing number of sources.