Analysing the Data-Driven Approach of Dynamically Estimating Positioning Accuracy

Abstract

The primary expectation from positioning systems is for them to provide the users with reliable estimates of their position. An additional piece of information that can greatly help the users utilize position estimates is the level of uncertainty that a positioning system assigns to the position estimate it produced. The concept of dynamically estimating the accuracy of position estimates of fingerprinting positioning systems has been sporadically discussed over the last decade in the literature of the field, where mainly handcrafted rules based on domain knowledge have been proposed. The emergence of IoT devices and the proliferation of data from Low Power Wide Area Networks (LPWANs) have facilitated the conceptualization of data-driven methods of determining the estimated certainty over position estimates. In this work, we analyze the data-driven approach of determining the Dynamic Accuracy Estimation (DAE), considering it in the broader context of a positioning system. More specifically, with the use of a public LoRaWAN dataset, the current work analyses: the repartition of the available training set between the tasks of determining the location estimates and the DAE, the concept of selecting a subset of the most reliable estimates, and the impact that the spatial distribution of the data has to the accuracy of the DAE. The work provides a wide overview of the data-driven approach of DAE determination in the context of the overall design of a positioning system.

Data-Driven Indoor Positioning Correction for Infrastructure-Enabled Autonomous Driving Systems: A Lifelong Framework

“…With the advent of IoT devices and the proliferation of data from low-power wide-area networks (LPWANs), the datadriven method has become a common positioning correction method [29]. For example, some improved K-nearest neighbor (KNN) algorithms are proposed for Wi-Fi-based positioning correction [30].…”