TY - GEN
T1 - Autonomous 3D UAV Localization using Taylor Series linearized TDOA-based approach with Machine Learning Algorithms
AU - Tilwari, Valmik
AU - Pack, Sangheon
N1 - 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.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Autonomous navigation
KW - Machine learning
KW - Optimization
KW - Time difference of arrival
KW - Unmanned aerial vehicles
UR - http://www.scopus.com/inward/record.url?scp=85143251316&partnerID=8YFLogxK
U2 - 10.1109/ICTC55196.2022.9952362
DO - 10.1109/ICTC55196.2022.9952362
M3 - Conference contribution
AN - SCOPUS:85143251316
T3 - International Conference on ICT Convergence
SP - 783
EP - 785
BT - ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
PB - IEEE Computer Society
T2 - 13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Y2 - 19 October 2022 through 21 October 2022
ER -