A fast blind SIMO channel identification algorithm for sparse sources

We address the blind identification of single-input-multiple output (SIMO) finite impulse response systems when the input signal is sparse. The problem is equivalent to underdetermined blind source separation (BSS), but with temporal correlation among the sources. Exploiting the sparse character of the input signal, the algorithm solves three different problems: first, to estimate the directions of the columns of the channel matrix; second, to estimate the L/sub 2/-norm of the columns; and finally, to find the correct ordering of the columns of the mixing matrix. The last step is not required for the blind source separation (BSS) problem, since any permutation of the columns is admissible for BSS. The performance and computational cost of the algorithm in a noiseless situation is compared against subspace-based techniques.