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© 2024 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Global positioning systems often fall short in dense forest environments, leading to increasing demand for innovative localization methods. Notably, existing methods suffer from the following limitations: (1) traditional localization frameworks necessitate several fixed anchors to estimate the locations of targets, which is difficult to satisfy in complex and uncertain forestry environments; (2) the uncertain environment severely decreases the quality of signal measurements and thus the localization accuracy. To cope with these limitations, this paper proposes a new method of trajectory localization for forestry environments with the assistance of UAVs. Based on the multi-agent DRL technique, the topology of UAVs is optimized in real-time to cater for high-accuracy target localization. Then, with the aid of RSS measurements from UAVs to the target, the least squares algorithm is used to estimate the location, which is more flexible and reliable than existing localization systems. Furthermore, a shared replay memory is incorporated into the proposed multi-agent DRL system, which can effectively enhance learning performance and efficiency. Simulation results show that the proposed method can obtain a flexible and high-accuracy localization system with the aid of UAVs, which exhibits better robustness against high-dimensional heterogeneous data and is suitable for forestry environments.

Details

Title
A Novel Method of UAV-Assisted Trajectory Localization for Forestry Environments
Author
Huang, Jian; Guo, Xiansheng  VIAFID ORCID Logo 
First page
3398
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3067437951
Copyright
© 2024 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.