Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs
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 unkn...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/1/3 |
| _version_ | 1797344290854141952 |
|---|---|
| author | Yang Gao Zhihong Gan Min Chen He Ma Xingpeng Mao |
| author_facet | Yang Gao Zhihong Gan Min Chen He Ma Xingpeng Mao |
| author_sort | Yang Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:00:12Z |
| format | Article |
| id | doaj.art-76b73fa3c5bf4315aefce0811a615b5f |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-08T11:00:12Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-76b73fa3c5bf4315aefce0811a615b5f2024-01-26T16:05:43ZengMDPI AGDrones2504-446X2023-12-0181310.3390/drones8010003Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVsYang Gao0Zhihong Gan1Min Chen2He Ma3Xingpeng Mao4School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaAccurate 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.https://www.mdpi.com/2504-446X/8/1/3UAV trackingUAV trajectory generationtrajectory predictioninteractive multi-model |
| spellingShingle | Yang Gao Zhihong Gan Min Chen He Ma Xingpeng Mao Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs Drones UAV tracking UAV trajectory generation trajectory prediction interactive multi-model |
| title | Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs |
| title_full | Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs |
| title_fullStr | Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs |
| title_full_unstemmed | Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs |
| title_short | Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs |
| title_sort | hybrid dual scale neural network model for tracking complex maneuvering uavs |
| topic | UAV tracking UAV trajectory generation trajectory prediction interactive multi-model |
| url | https://www.mdpi.com/2504-446X/8/1/3 |
| work_keys_str_mv | AT yanggao hybriddualscaleneuralnetworkmodelfortrackingcomplexmaneuveringuavs AT zhihonggan hybriddualscaleneuralnetworkmodelfortrackingcomplexmaneuveringuavs AT minchen hybriddualscaleneuralnetworkmodelfortrackingcomplexmaneuveringuavs AT hema hybriddualscaleneuralnetworkmodelfortrackingcomplexmaneuveringuavs AT xingpengmao hybriddualscaleneuralnetworkmodelfortrackingcomplexmaneuveringuavs |