  We consider the problem of estimating an RF-device's location based on
observations, such as received signal strength, from a set of transmitters with
known locations. We survey the literature on this problem, showing that
previous authors have considered implicitly or explicitly various metrics. We
present a novel Bayesian framework that unifies these works and shows how to
optimize the location estimation with respect to a given metric. We demonstrate
how the framework can incorporate a general class of algorithms, including both
model-based methods and data-driven algorithms such fingerprinting. This is
illustrated by re-deriving the most popular algorithms within this framework.
When used with a data-driven approach, our framework has cognitive
self-improving properties in that it provably improves with increasing data
compared to traditional methods. Furthermore, we propose using the error-CDF as
a unified way of comparing algorithms based on two methods: (i) stochastic
dominance, and (ii) an upper bound on error-CDFs. We prove that an algorithm
that optimizes any distance based cost function is not stochastically dominated
by any other algorithm. This suggests that in lieu of the search for a
universally best localization algorithm, the community should focus on finding
the best algorithm for a given well-defined objective.
