Performance analysis of IPNLMS for identification of time-varying systems

The tracking performance of adaptive filters is crucially important in practical applications involving time-varying systems. We present an analysis of the tracking performance for IPNLMS, one of the best known and best performing algorithms originally targeted at sparse system identification. We then validate our analytic results in practical simulations for echo cancellation for sparse and dispersive time-varying unknown echo path systems. These results show the analysis to be highly accurate in all the cases studied.