التفاصيل البيبلوغرافية
| العنوان: |
Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs |
| المؤلفون: |
Yang Gao, Zhihong Gan, Min Chen, He Ma, Xingpeng Mao |
| المصدر: |
Drones, Vol 8, Iss 1, p 3 (2023) |
| بيانات النشر: |
MDPI AG, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Motor vehicles. Aeronautics. Astronautics |
| مصطلحات موضوعية: |
UAV tracking, UAV trajectory generation, trajectory prediction, interactive multi-model, Motor vehicles. Aeronautics. Astronautics, TL1-4050 |
| الوصف: |
Accurate tracking and predicting unmanned aerial vehicle (UAV) trajectories are essential to ensure mission success, equipment safety, and data accuracy. Maneuverable UAVs exhibit complex and dynamic motion, and conventional tracking algorithms that rely on predefined models perform poorly when unknown parameters are used. To address this issue, this paper introduces a hybrid dual-scale neural network model based on the generalized regression multi-model and cubature information filter (GRMM-CIF) framework. We have established the GRMM-CIF filtering structure to differentiate motion modes and reduce measurement noise. Furthermore, considering trajectory datasets and rates of motion change, a neural network at different scales will be designed. We propose the dual-scale bidirectional long short-term memory (DS-Bi-LSTM) algorithm to address prediction delays in a multi-model context. Additionally, we employ scale sliding windows and threshold-based decision-making to achieve dual-scale trajectory reconstruction, ultimately enhancing tracking accuracy. Simulation results confirm the effectiveness of our approach in handling the uncertainty of UAV motion and achieving precise estimations. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2504-446X |
| Relation: |
https://www.mdpi.com/2504-446X/8/1/3; https://doaj.org/toc/2504-446X |
| DOI: |
10.3390/drones8010003 |
| URL الوصول: |
https://doaj.org/article/76b73fa3c5bf4315aefce0811a615b5f |
| رقم الانضمام: |
edsdoj.76b73fa3c5bf4315aefce0811a615b5f |
| قاعدة البيانات: |
Directory of Open Access Journals |