Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework
Aircraft trajectory prediction is challenging because of the flight process with uncertain kinematic motion and varying dynamics, which is characterized by intricate temporal dependencies of the flight surveillance data. To address these challenges, this study proposes a novel hybrid prediction fram...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2531 |
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| _version_ | 1849713534016946176 |
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| author | Fan Li Xuezhi Xu Rihan Wang Mingyuan Ma Zijing Dong |
| author_facet | Fan Li Xuezhi Xu Rihan Wang Mingyuan Ma Zijing Dong |
| author_sort | Fan Li |
| collection | DOAJ |
| description | Aircraft trajectory prediction is challenging because of the flight process with uncertain kinematic motion and varying dynamics, which is characterized by intricate temporal dependencies of the flight surveillance data. To address these challenges, this study proposes a novel hybrid prediction framework, the IMM-Informer, which integrates an interacting multiple model (IMM) approach with the deep learning-based Informer model. The IMM processes flight tracking with multiple typical motion models to produce the initial state predictions. Within the Informer framework, the encoder captures the temporal features with the ProbSparse self-attention mechanism, and the decoder generates trajectory deviation predictions. A final fusion combines the IMM estimators with Informer correction outputs and leverages their respective strengths to achieve accurate and robust predictions. The experiments are conducted using real flight surveillance data received from automatic dependent surveillance-broadcast (ADS-B) sensors to validate the effectiveness of the proposed method. The results demonstrate that the IMM-Informer framework has notable prediction error reductions and significantly outperforms the prediction accuracies of the standalone sequence prediction network models. |
| format | Article |
| id | doaj-art-4ee91efa81074930922e95b1a0b292b1 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4ee91efa81074930922e95b1a0b292b12025-08-20T03:13:57ZengMDPI AGSensors1424-82202025-04-01258253110.3390/s25082531Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer FrameworkFan Li0Xuezhi Xu1Rihan Wang2Mingyuan Ma3Zijing Dong4CAAC Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan 618307, ChinaCAAC Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan 618307, ChinaCAAC Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Mathematics, Sichuan University, Chengdu 610065, ChinaAircraft trajectory prediction is challenging because of the flight process with uncertain kinematic motion and varying dynamics, which is characterized by intricate temporal dependencies of the flight surveillance data. To address these challenges, this study proposes a novel hybrid prediction framework, the IMM-Informer, which integrates an interacting multiple model (IMM) approach with the deep learning-based Informer model. The IMM processes flight tracking with multiple typical motion models to produce the initial state predictions. Within the Informer framework, the encoder captures the temporal features with the ProbSparse self-attention mechanism, and the decoder generates trajectory deviation predictions. A final fusion combines the IMM estimators with Informer correction outputs and leverages their respective strengths to achieve accurate and robust predictions. The experiments are conducted using real flight surveillance data received from automatic dependent surveillance-broadcast (ADS-B) sensors to validate the effectiveness of the proposed method. The results demonstrate that the IMM-Informer framework has notable prediction error reductions and significantly outperforms the prediction accuracies of the standalone sequence prediction network models.https://www.mdpi.com/1424-8220/25/8/2531flight trajectoryADS-B datahybrid predictioninteracting multiple modelInformer |
| spellingShingle | Fan Li Xuezhi Xu Rihan Wang Mingyuan Ma Zijing Dong Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework Sensors flight trajectory ADS-B data hybrid prediction interacting multiple model Informer |
| title | Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework |
| title_full | Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework |
| title_fullStr | Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework |
| title_full_unstemmed | Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework |
| title_short | Flight Trajectory Prediction Based on Automatic Dependent Surveillance-Broadcast Data Fusion with Interacting Multiple Model and Informer Framework |
| title_sort | flight trajectory prediction based on automatic dependent surveillance broadcast data fusion with interacting multiple model and informer framework |
| topic | flight trajectory ADS-B data hybrid prediction interacting multiple model Informer |
| url | https://www.mdpi.com/1424-8220/25/8/2531 |
| work_keys_str_mv | AT fanli flighttrajectorypredictionbasedonautomaticdependentsurveillancebroadcastdatafusionwithinteractingmultiplemodelandinformerframework AT xuezhixu flighttrajectorypredictionbasedonautomaticdependentsurveillancebroadcastdatafusionwithinteractingmultiplemodelandinformerframework AT rihanwang flighttrajectorypredictionbasedonautomaticdependentsurveillancebroadcastdatafusionwithinteractingmultiplemodelandinformerframework AT mingyuanma flighttrajectorypredictionbasedonautomaticdependentsurveillancebroadcastdatafusionwithinteractingmultiplemodelandinformerframework AT zijingdong flighttrajectorypredictionbasedonautomaticdependentsurveillancebroadcastdatafusionwithinteractingmultiplemodelandinformerframework |