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Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-...

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Autores principales: Zhu, Xudong, Lai, Jizhou, Chen, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573180/
https://www.ncbi.nlm.nih.gov/pubmed/36236224
http://dx.doi.org/10.3390/s22197125
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author Zhu, Xudong
Lai, Jizhou
Chen, Sheng
author_facet Zhu, Xudong
Lai, Jizhou
Chen, Sheng
author_sort Zhu, Xudong
collection PubMed
description The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.
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spelling pubmed-95731802022-10-17 Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector Zhu, Xudong Lai, Jizhou Chen, Sheng Sensors (Basel) Article The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm. MDPI 2022-09-20 /pmc/articles/PMC9573180/ /pubmed/36236224 http://dx.doi.org/10.3390/s22197125 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Xudong
Lai, Jizhou
Chen, Sheng
Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector
title Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector
title_full Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector
title_fullStr Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector
title_full_unstemmed Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector
title_short Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector
title_sort cooperative location method for leader-follower uav formation based on follower uav’s moving vector
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573180/
https://www.ncbi.nlm.nih.gov/pubmed/36236224
http://dx.doi.org/10.3390/s22197125
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AT laijizhou cooperativelocationmethodforleaderfolloweruavformationbasedonfolloweruavsmovingvector
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