Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion
The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, pr...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/14/3276 |
| _version_ | 1797416180772765696 |
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| author | Weiming Tian Linlin Fang Weidong Li Na Ni Rui Wang Cheng Hu Hanzhe Liu Weigang Luo |
| author_facet | Weiming Tian Linlin Fang Weidong Li Na Ni Rui Wang Cheng Hu Hanzhe Liu Weigang Luo |
| author_sort | Weiming Tian |
| collection | DOAJ |
| description | The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion. |
| first_indexed | 2024-03-09T05:59:47Z |
| format | Article |
| id | doaj.art-f58be6231a7c417890582614911d8938 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:59:47Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f58be6231a7c417890582614911d89382023-12-03T12:10:17ZengMDPI AGRemote Sensing2072-42922022-07-011414327610.3390/rs14143276Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering MotionWeiming Tian0Linlin Fang1Weidong Li2Na Ni3Rui Wang4Cheng Hu5Hanzhe Liu6Weigang Luo7Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaThe effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.https://www.mdpi.com/2072-4292/14/14/3276deep learningmultiple modelmaneuvering target tracking |
| spellingShingle | Weiming Tian Linlin Fang Weidong Li Na Ni Rui Wang Cheng Hu Hanzhe Liu Weigang Luo Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion Remote Sensing deep learning multiple model maneuvering target tracking |
| title | Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion |
| title_full | Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion |
| title_fullStr | Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion |
| title_full_unstemmed | Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion |
| title_short | Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion |
| title_sort | deep learning based multiple model tracking method for targets with complex maneuvering motion |
| topic | deep learning multiple model maneuvering target tracking |
| url | https://www.mdpi.com/2072-4292/14/14/3276 |
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