  We formulate antenna impedance estimation in a classical estimation framework
under correlated Raleigh fading channels. Based on training sequences of
multiple packets, we derive the ML estimators for antenna impedance and channel
variance, treating the fading path gains as nuisance parameters. These ML
estimators can be found via scalar optimization. We explore the efficiency of
these estimators against Cramer-Rao lower bounds by numerical examples. The
impact of channel correlation on impedance estimation accuracy is investigated.
