Nonlinear autoregressive modeling of non-Gaussian signals using l/sub p/-norm techniques

For the estimation of the model coefficients of a polynomial autoregressive process with non-Gaussian innovations least l/sub p/-norm estimation (LLPN) is suggested. Simulations showed that LLPN estimation leads to better estimates than the least squares estimation in terms of the mean and the standard deviations of the estimates. The algorithm is also employed in modeling audio data in non-Gaussian noise with the objective of separating signal from noise and superior results have been obtained when compared to the linear autoregressive modeling. Directions of future research are also addressed.