A finite-step global convergence algorithm for the cumulant-based parameter estimation of multichannel moving average processes

An iterative algorithm for the identification of multichannel moving average (MA) processes driven by mutually independent and identically distributed (i.i.d.) input signals is proposed. It is shown that the algorithm has a finite-step global convergence property. This algorithm is computationally efficient and numerically stable. Two multichannel MA models, including one nonminimum-phase MA model, are estimated by this algorithm with satisfactory performances. It is shown that this algorithm guarantees a solution of third-order cumulant-based identification equations