Nouvelles approches ď estimation des paramètres autorégressifs variables utilisant les cumulants

In this paper, we present two new cumulant-based methods for time-varying ar parameter estimation : a batch-type evolutive method and an adaptive gradienttype algorithm. The evaluation of these techniques is performed through simulations on synthetic signals in free-noise case and when data are corrupted by an additive, zero-mean, Gaussian white noise. We compare them to their autocorrelation-based counterparts. The obtained results show, when using an appropriate criterion, the superiority of the cumulant-based evolutive method over both its autocorrelation-based counterpart and the cumulant-based gradient-type algorithm at expense of a great computational complexity.