Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation
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.
Recommended Citation
F. B. Sorbelli et al., "Measurement Errors in Range-Based Localization Algorithms for UAVs: Analysis and Experimentation," IEEE Transactions on Mobile Computing, vol. 21, no. 4, pp. 1291 - 1304, Institute of Electrical and Electronics Engineers (IEEE), Apr 2022.
The definitive version is available at https://doi.org/10.1109/TMC.2020.3020584
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Drone; Ground Error; Localization Algorithm; Localization Error; Range-Based; Trilateration Error
International Standard Serial Number (ISSN)
1558-0660; 1536-1233
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2022 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Apr 2022
Comments
This work was supported in part by Project NALP-SAPR granted by FSE, Project NALP-SAPR2 granted by University of Perugia, by NATO grant G4936, Intelligent Systems Center (ISC) at Missouri S&T, and by NSF grants CNS-1545050, CNS-1725755, CNS-1818942, and SCC-1952045.