| Title: |
Short-Term Target Maneuvering Trajectory Prediction Using DTW–CNN–LSTM |
| Authors: |
Haifeng Guo, Jinyi Yang, Xianyong Jing, Peng Zhang |
| Source: |
International Journal of Aerospace Engineering, Vol 2025 (2025) |
| Publisher Information: |
Wiley, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Motor vehicles. Aeronautics. Astronautics |
| Subject Terms: |
Motor vehicles. Aeronautics. Astronautics, TL1-4050 |
| More Details: |
This paper introduces a novel prediction model designed to mitigate the substantial data dependency associated with maneuver trajectory prediction in unmanned combat air vehicles (UCAVs) during air combat. Considering the characteristics of high noise, dynamic complexity, and variable data lengths inherent in short-range air combat scenarios, we employ dynamic time warping (DTW) to assess the similarity of 3D trajectory data. This approach allows us to identify and select the most analogous historical data, which we then utilize as our training dataset. In pursuit of enhanced precision for online trajectory prediction, we propose an improved convolutional neural network (CNN) that not only offers “after-zero” information but also incorporates delay compensation mechanisms. Our experimental findings indicate that the proposed prediction model not only satisfies the stringent timeliness requirements but also outperforms benchmark models in terms of prediction accuracy across various operating conditions. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1687-5974 |
| Relation: |
https://doaj.org/toc/1687-5974 |
| DOI: |
10.1155/ijae/6484090 |
| Access URL: |
https://doaj.org/article/4652505ee6cc45d28854e4185966cb6c |
| Accession Number: |
edsdoj.4652505ee6cc45d28854e4185966cb6c |
| Database: |
Directory of Open Access Journals |
