  The problem of astrometry is revisited from the perspective of analyzing the
attainability of well-known performance limits (the Cramer-Rao bound) for the
estimation of the relative position of light-emitting (usually point-like)
sources on a CCD-like detector using commonly adopted estimators such as the
weighted least squares and the maximum likelihood. Novel technical results are
presented to determine the performance of an estimator that corresponds to the
solution of an optimization problem in the context of astrometry. Using these
results we are able to place stringent bounds on the bias and the variance of
the estimators in close form as a function of the data. We confirm these
results through comparisons to numerical simulations under a broad range of
realistic observing conditions. The maximum likelihood and the weighted least
square estimators are analyzed. We confirm the sub-optimality of the weighted
least squares scheme from medium to high signal-to-noise found in an earlier
study for the (unweighted) least squares method. We find that the maximum
likelihood estimator achieves optimal performance limits across a wide range of
relevant observational conditions. Furthermore, from our results, we provide
concrete insights for adopting an adaptive weighted least square estimator that
can be regarded as a computationally efficient alternative to the optimal
maximum likelihood solution. We provide, for the first time, close-form
analytical expressions that bound the bias and the variance of the weighted
least square and maximum likelihood implicit estimators for astrometry using a
Poisson-driven detector. These expressions can be used to formally assess the
precision attainable by these estimators in comparison with the minimum
variance bound.
