Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering
To improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9874805/ |
| _version_ | 1811270129925750784 |
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| author | Gang Zhong Honghai Zhang Jiangying Zhou Jinlun Zhou Hao Liu |
| author_facet | Gang Zhong Honghai Zhang Jiangying Zhou Jinlun Zhou Hao Liu |
| author_sort | Gang Zhong |
| collection | DOAJ |
| description | To improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is first designed to cluster the UAV trajectory segments divided by a fixed time window. Each trajectory segment is given a category label that represents some certain type of behavior characteristics, such as climbing, turning, descending, etc. The convolutional neural network (CNN) is used to identify the category label of a given trajectory segment by learning the behavior characteristics of different trajectory segments. Based on the long-short-term memory network (LSTM), a short-term trajectory prediction model for different categories of label trajectory segments is established. The global trajectory prediction includes several steps adopting the corresponding prediction models. Historical trajectory data of UAVs are used to validate the proposed prediction method. Experiment results indicate that the method can obtain obviously better prediction accuracy in a short prediction time range (0-3s) with acceptable efficiency compared to LSTM, GRU and velocity trend extrapolation. |
| first_indexed | 2024-04-12T21:55:10Z |
| format | Article |
| id | doaj.art-a39879b02b364a818f5378f876110b3b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T21:55:10Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a39879b02b364a818f5378f876110b3b2022-12-22T03:15:21ZengIEEEIEEE Access2169-35362022-01-0110933629338010.1109/ACCESS.2022.32034289874805Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory ClusteringGang Zhong0https://orcid.org/0000-0003-1733-5897Honghai Zhang1Jiangying Zhou2Jinlun Zhou3https://orcid.org/0000-0002-8799-1956Hao Liu4College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaTo improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is first designed to cluster the UAV trajectory segments divided by a fixed time window. Each trajectory segment is given a category label that represents some certain type of behavior characteristics, such as climbing, turning, descending, etc. The convolutional neural network (CNN) is used to identify the category label of a given trajectory segment by learning the behavior characteristics of different trajectory segments. Based on the long-short-term memory network (LSTM), a short-term trajectory prediction model for different categories of label trajectory segments is established. The global trajectory prediction includes several steps adopting the corresponding prediction models. Historical trajectory data of UAVs are used to validate the proposed prediction method. Experiment results indicate that the method can obtain obviously better prediction accuracy in a short prediction time range (0-3s) with acceptable efficiency compared to LSTM, GRU and velocity trend extrapolation.https://ieeexplore.ieee.org/document/9874805/UAV4D trajectory predictionspatio-temporal clusteringdeep learning |
| spellingShingle | Gang Zhong Honghai Zhang Jiangying Zhou Jinlun Zhou Hao Liu Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering IEEE Access UAV 4D trajectory prediction spatio-temporal clustering deep learning |
| title | Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering |
| title_full | Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering |
| title_fullStr | Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering |
| title_full_unstemmed | Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering |
| title_short | Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering |
| title_sort | short term 4d trajectory prediction for uav based on spatio temporal trajectory clustering |
| topic | UAV 4D trajectory prediction spatio-temporal clustering deep learning |
| url | https://ieeexplore.ieee.org/document/9874805/ |
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