  The problem of target localization with noise is addressed. The target is a
sample from a continuous random variable with known distribution and the goal
is to locate it with minimum mean squared error distortion. The localization
scheme or policy proceeds by queries, or questions, weather or not the target
belongs to some subset as it is addressed in the 20-question framework. These
subsets are not constrained to be intervals and the answers to the queries are
noisy. While this situation is well studied for adaptive querying, this paper
is focused on the non adaptive querying policies based on dyadic questions. The
asymptotic minimum achievable distortion under such policies is derived.
Furthermore, a policy named the Aurelian1 is exhibited which achieves
asymptotically this distortion.
