An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network
Accurate track segment association plays an important role in modern sensor data processing systems to ensure the temporal and spatial consistency of target information. Traditional methods face a series of challenges in association accuracy when handling complex scenarios involving short tracks or...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3465 |
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| author | Jiadi Qi Xiaoke Lu Jinping Sun |
| author_facet | Jiadi Qi Xiaoke Lu Jinping Sun |
| author_sort | Jiadi Qi |
| collection | DOAJ |
| description | Accurate track segment association plays an important role in modern sensor data processing systems to ensure the temporal and spatial consistency of target information. Traditional methods face a series of challenges in association accuracy when handling complex scenarios involving short tracks or multi-target intersections. This study proposes an intelligent association method that includes a multi-dimensional track data preprocessing algorithm and the characteristic-aware attention long short-term memory (CA-LSTM) network. The algorithm can segment and temporally align track segments containing multi-dimensional characteristics. The CA-LSTM model is built to perform track segment association and has two basic parts. One part focuses on the target characteristic dimension and utilizes the separation and importance evaluation of physical characteristics to make association decisions. The other part focuses on the time dimension, matching the application scenarios of short, medium and long tracks by obtaining the temporal characteristics of different time spans. The method is verified on a multi-source track association dataset. Experimental results show that association accuracy rate is 85.19% for short-range track segments and 96.97% for long-range track segments. Compared with the typical traditional method LSTM, this method has a 9.89% improvement in accuracy on short tracks. |
| format | Article |
| id | doaj-art-c858bfc63b6741f18c69caa2a8c8b24c |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c858bfc63b6741f18c69caa2a8c8b24c2025-08-20T02:32:57ZengMDPI AGSensors1424-82202025-05-012511346510.3390/s25113465An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM NetworkJiadi Qi0Xiaoke Lu1Jinping Sun2Nanjing Research Institute of Electronic Technology, Nanjing 610500, ChinaNanjing Research Institute of Electronic Technology, Nanjing 610500, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaAccurate track segment association plays an important role in modern sensor data processing systems to ensure the temporal and spatial consistency of target information. Traditional methods face a series of challenges in association accuracy when handling complex scenarios involving short tracks or multi-target intersections. This study proposes an intelligent association method that includes a multi-dimensional track data preprocessing algorithm and the characteristic-aware attention long short-term memory (CA-LSTM) network. The algorithm can segment and temporally align track segments containing multi-dimensional characteristics. The CA-LSTM model is built to perform track segment association and has two basic parts. One part focuses on the target characteristic dimension and utilizes the separation and importance evaluation of physical characteristics to make association decisions. The other part focuses on the time dimension, matching the application scenarios of short, medium and long tracks by obtaining the temporal characteristics of different time spans. The method is verified on a multi-source track association dataset. Experimental results show that association accuracy rate is 85.19% for short-range track segments and 96.97% for long-range track segments. Compared with the typical traditional method LSTM, this method has a 9.89% improvement in accuracy on short tracks.https://www.mdpi.com/1424-8220/25/11/3465sensor data processingtrack segment associationcharacteristic-aware attentiongated adaptive long short-term memory |
| spellingShingle | Jiadi Qi Xiaoke Lu Jinping Sun An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network Sensors sensor data processing track segment association characteristic-aware attention gated adaptive long short-term memory |
| title | An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network |
| title_full | An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network |
| title_fullStr | An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network |
| title_full_unstemmed | An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network |
| title_short | An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network |
| title_sort | intelligent track segment association method based on characteristic aware attention lstm network |
| topic | sensor data processing track segment association characteristic-aware attention gated adaptive long short-term memory |
| url | https://www.mdpi.com/1424-8220/25/11/3465 |
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