A Unifying Bayesian Optimization Framework for Radio Frequency Localization

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 Bayesian optimization 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. Furthermore, we propose using the error-CDF as a unified way of comparing algorithms based on two methods: 1) stochastic dominance and 2) an upper bound on error-CDFs. We prove that an algorithm that optimizes any distance-based cost function is not strictly 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.