# A Unifying Bayesian Optimization Framework for Radio Frequency Localization

## TL;DR

This paper introduces a unifying Bayesian framework for RF localization that encompasses various existing methods, allowing for optimized, data-driven, and self-improving localization algorithms with a new way to compare their performance.

## Contribution

The authors present a novel Bayesian framework that unifies existing RF localization algorithms and enables optimization for specific metrics, including data-driven self-improvement and a new comparison method.

## Key findings

- The framework can incorporate both model-based and data-driven algorithms.
- Data-driven algorithms within the framework improve with more data.
- A new method using error-CDFs allows for effective algorithm comparison.

## Abstract

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.