Factor graph-based technique for trajectory tracking of target with high mobility
Jiang, Lei; Keerativoranan, Nopphon; Matsumoto, Tad; Takada, Jun-Ichi (2024-09-10)
Institute of Electronics, Information and Communication Engineers
10.09.2024
L. Jiang, N. Keerativoranan, T. Matsumoto and J. -I. Takada, "Factor graph-based technique for trajectory tracking of target with high mobility," in IEICE Communications Express, doi: 10.23919/comex.2024XBL0132
https://creativecommons.org/licenses/by-nc-nd/4.0/
This work is licensed under a Creative Commons Attribution Non Commercial, No Derivatives 4.0 License. Copyright © 2023 The Institute of Electronics, Information and Communication Engineers.
https://creativecommons.org/licenses/by-nc-nd/4.0/
This work is licensed under a Creative Commons Attribution Non Commercial, No Derivatives 4.0 License. Copyright © 2023 The Institute of Electronics, Information and Communication Engineers.
https://creativecommons.org/licenses/by-nc-nd/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202409306129
https://urn.fi/URN:NBN:fi:oulu-202409306129
Tiivistelmä
Abstract
This paper presents a trajectory tracking algorithm for high-mobility targets using an extended Kalman smoothing (EKS)-based factor graph (FG). Traditional tracking methods often face challenges in maintaining accuracy and computational efficiency when dealing with fast-moving objects. Leveraging the probabilistic framework of factor graphs and robust estimation of EKS' the algorithm enhances tracking precision for fast-moving objects. Extensive simulations across various motion models demonstrate improved accuracy and robustness. The results indicate that this method effectively addresses the limitations of conventional tracking algorithms' providing a promising solution for applications in aviation' autonomous vehicles' and other domains requiring high-mobility tracking.
This paper presents a trajectory tracking algorithm for high-mobility targets using an extended Kalman smoothing (EKS)-based factor graph (FG). Traditional tracking methods often face challenges in maintaining accuracy and computational efficiency when dealing with fast-moving objects. Leveraging the probabilistic framework of factor graphs and robust estimation of EKS' the algorithm enhances tracking precision for fast-moving objects. Extensive simulations across various motion models demonstrate improved accuracy and robustness. The results indicate that this method effectively addresses the limitations of conventional tracking algorithms' providing a promising solution for applications in aviation' autonomous vehicles' and other domains requiring high-mobility tracking.
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