“…The probability density function of each sample can be thus written as (9) Note that, as (7) and (4) show, holds the parametric dependance with the AR parameters . The log-likelihood of the set , present in (8), is thus (10) The gradient of the log-likelihood for the th sample is equal to the following expression: (11) where corresponds to the Wiener filter (12) and (13) The objective of this section is to deduce the asymptotic FIM, which is in [6] defined as follows: (14) where (15) Based on the expansion (10), and given that the expected value of the information gradient (11) vanishes, that is , the previous term (15) can be expanded and simplified to (16) By substituting the information gradient (11) into (16), this term becomes (17) The complex Gaussian random variable fulfills (18) which allows us to simplify (17) to (19) On the other hand, the factor can be expanded to (20)…”