Subspace methods for blind estimation of timevarying FIR channels

Novel linear algorithms are proposed in this paper for estimating timevarying FIR systems, without resorting to higher order statistics. The proposed methods are applicable to systems where each timevarying tap coefficient can be described (with respect to time) as a linear combination of a finite number of basis functions. Examples of such channels include almost periodically varying ones (Fourier Series description) or channels locally modeled by a truncated Taylor series or by a wavelet expansion. It is shown that the estimation of the expansion parameters is equivalent to estimating the secondorder parameters of an unobservable FIR singleinputmanyoutput (SIMO) process, which are directly computed (under some assumptions) from the observation data. By exploiting this equivalence, a number of different blind subspace methods are applicable, which have been originally developed in the context of timeinvariant SIMO systems. Identifiability issues are investigated, and some illustrative simulations are presented.