An online quasi-Newton algorithm for blind SIMO identification

In the last decade various time- and frequency-domain algorithms were derived to blindly identify acoustic systems. One of these algorithms is the multichannel Newton (MCN) algorithm, which is also the basis of the well known normalized multichannel frequency-domain least-mean-square (NMCFLMS) algorithm. A major drawback of the MCN is that it requires the computation and inversion of a Hessian matrix, which involves extensive computation making it unsuitable for online applications. In this paper, we therefore derive and investigate an efficient online multichannel quasi-Newton (MCQN) algorithm that updates the inverse of the Hessian by analyzing successive gradient vectors. The new MCQN is shown to exhibit similar performance to MCN but with much reduced complexity.