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Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters
This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144096/ https://www.ncbi.nlm.nih.gov/pubmed/37112289 http://dx.doi.org/10.3390/s23083948 |
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author | Luo, Wei Zhao, Yongxiang Shao, Quanqin Li, Xiaoliang Wang, Dongliang Zhang, Tongzuo Liu, Fei Duan, Longfang He, Yuejun Wang, Yancang Zhang, Guoqing Wang, Xinghui Yu, Zhongde |
author_facet | Luo, Wei Zhao, Yongxiang Shao, Quanqin Li, Xiaoliang Wang, Dongliang Zhang, Tongzuo Liu, Fei Duan, Longfang He, Yuejun Wang, Yancang Zhang, Guoqing Wang, Xinghui Yu, Zhongde |
author_sort | Luo, Wei |
collection | PubMed |
description | This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (f, Q, and R) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation. |
format | Online Article Text |
id | pubmed-10144096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101440962023-04-29 Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters Luo, Wei Zhao, Yongxiang Shao, Quanqin Li, Xiaoliang Wang, Dongliang Zhang, Tongzuo Liu, Fei Duan, Longfang He, Yuejun Wang, Yancang Zhang, Guoqing Wang, Xinghui Yu, Zhongde Sensors (Basel) Article This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (f, Q, and R) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation. MDPI 2023-04-13 /pmc/articles/PMC10144096/ /pubmed/37112289 http://dx.doi.org/10.3390/s23083948 Text en © 2023 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 Luo, Wei Zhao, Yongxiang Shao, Quanqin Li, Xiaoliang Wang, Dongliang Zhang, Tongzuo Liu, Fei Duan, Longfang He, Yuejun Wang, Yancang Zhang, Guoqing Wang, Xinghui Yu, Zhongde Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
title | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
title_full | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
title_fullStr | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
title_full_unstemmed | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
title_short | Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters |
title_sort | procapra przewalskii tracking autonomous unmanned aerial vehicle based on improved long and short-term memory kalman filters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10144096/ https://www.ncbi.nlm.nih.gov/pubmed/37112289 http://dx.doi.org/10.3390/s23083948 |
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