Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation

TL;DR

This paper analyzes measurement errors in range-based UAV localization, derives error bounds, extends algorithms for better accuracy, and validates findings through experiments with UWB-equipped drones and ground devices.

Contribution

It provides analytical error models for UAV localization algorithms, extends range-free methods for improved accuracy, and validates results with real-world experiments.

Findings

Analytical error bounds closely match experimental results.

Extended algorithms significantly improve localization accuracy.

Measurement errors impact ground distance estimations in UAV localization.

Abstract

Localizing ground devices (GDs) is an important requirement for a wide variety of applications, such as infrastructure monitoring, precision agriculture, search and rescue operations, to name a few. To this end, unmanned aerial vehicles (UAVs) or drones offer a promising technology due to their flexibility. However, the distance measurements performed using a drone, an integral part of a localization procedure, incur several errors that affect the localization accuracy. In this paper, we provide analytical expressions for the impact of different kinds of measurement errors on the ground distance between the UAV and GDs. We review three range-based and three range-free localization algorithms, identify their source of errors, and analytically derive the error bounds resulting from aggregating multiple inaccurate measurements. We then extend the range-free algorithms for improved accuracy. We validate our theoretical analysis and compare the observed localization error of the algorithms after collecting data from a testbed using ten GDs and one drone, equipped with ultra wide band (UWB) antennas and operating in an open field. Results show that our analysis closely matches with experimental localization errors. Moreover, compared to their original counterparts, the extended range-free algorithms significantly improve the accuracy.