Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms

Valmik Tilwari; Sangheon Pack

doi:10.1109/ICTC55196.2022.9952362

Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms

Valmik Tilwari, Sangheon Pack

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Time difference of arrival (TDOA) is the prominent technology for autonomous and real-time three-dimensional (3D) location estimation of the Unmanned aerial vehicles (UAVs). Conventional TDOA localization techniques suffer from the nonlinear optimization problem and hyperbolic intersection to predict the 3D location of UAVs precisely. Therefore, this paper proposes a new positioning Taylor series linearized TDOA-based approach to estimate a precise 3D position of the UAV s. The proposed approach determines the 3D location of the UAV s by evaluating the synchronized difference in arrival time of the signal at spatially separated various anchors nodes. Moreover, machine learning algorithms are applied to optimize the performance of the Taylor series linearized TDOA-based approach and provide a localization solution with comparable accuracy in real-time applications. Consequently, the simulation results are expressed in terms of root mean square errors compared with various machine learning algorithms.

Original language	English
Title of host publication	ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Publisher	IEEE Computer Society
Pages	783-785
Number of pages	3
ISBN (Electronic)	9781665499392
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952362
Publication status	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 2022 Oct 19 → 2022 Oct 21

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	22/10/19 → 22/10/21

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This research was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-0-01810) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and in part by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2021R1A4A3022102).

Publisher Copyright:
© 2022 IEEE.

Keywords

Autonomous navigation
Machine learning
Optimization
Time difference of arrival
Unmanned aerial vehicles

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC55196.2022.9952362

Cite this

Tilwari, V., & Pack, S. (2022). Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation (pp. 783-785). (International Conference on ICT Convergence; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTC55196.2022.9952362

Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms. / Tilwari, Valmik; Pack, Sangheon.
ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. p. 783-785 (International Conference on ICT Convergence; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tilwari, V & Pack, S 2022, Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms. in ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. International Conference on ICT Convergence, vol. 2022-October, IEEE Computer Society, pp. 783-785, 13th International Conference on Information and Communication Technology Convergence, ICTC 2022, Jeju Island, Korea, Republic of, 22/10/19. https://doi.org/10.1109/ICTC55196.2022.9952362

Tilwari V, Pack S. Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society. 2022. p. 783-785. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952362

Tilwari, Valmik ; Pack, Sangheon. / Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms. ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. pp. 783-785 (International Conference on ICT Convergence).

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abstract = "Time difference of arrival (TDOA) is the prominent technology for autonomous and real-time three-dimensional (3D) location estimation of the Unmanned aerial vehicles (UAVs). Conventional TDOA localization techniques suffer from the nonlinear optimization problem and hyperbolic intersection to predict the 3D location of UAVs precisely. Therefore, this paper proposes a new positioning Taylor series linearized TDOA-based approach to estimate a precise 3D position of the UAV s. The proposed approach determines the 3D location of the UAV s by evaluating the synchronized difference in arrival time of the signal at spatially separated various anchors nodes. Moreover, machine learning algorithms are applied to optimize the performance of the Taylor series linearized TDOA-based approach and provide a localization solution with comparable accuracy in real-time applications. Consequently, the simulation results are expressed in terms of root mean square errors compared with various machine learning algorithms.",

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Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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