Jordi Solé i Casals (1), Christian Jutten(2), Anisse Taleb (2)
The prediction filters are well known models for signal
estimation, in communications, control and many others
areas. The classical method for deriving linear
prediction coding (LPC) filters is often based on the
minimization of a mean square error (MSE).
Consequently, second order statistics are only required,
but the estimation is only optimal if the residue is
independent and identically distributed (iid) Gaussian.
In this paper, we derive the ML estimate of the
prediction filter. Relationships with robust estimation of
autoregressive (AR) processes, with blind
deconvolution and with source separation based on
mutual information minimization are then detailed. The
algorithm, based on the minimization of a highorder
statistics criterion, uses online estimation of the residue
statistics. Experimental results emphasize on the
interest of this approach.