The application of Volterra series to signal estimation

The authors examine the problem of estimating signals corrupted by additive non-Gaussian noise. Since the linear filter is known to be optimal if the noise is Gaussian, they apply general nonlinear filters, based on Volterra series, to the non-Gaussian case. Nonlinear Wiener filters are derived, and their performance investigated in example non-Gaussian noise densities.<
>