A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost Function
This paper proposes a trajectory prediction method via the adaptive cost function to address the difficulties in inferring the attack intention and maneuver mode, as well as the accumulation of prediction error during the trajectory prediction of reentry glide vehicles. Firstly, the vehicle guidance...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/12/1/62 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589502808326144 |
|---|---|
| author | Yangchao He Jiong Li Lei Shao Chijun Zhou Xiangwei Bu |
| author_facet | Yangchao He Jiong Li Lei Shao Chijun Zhou Xiangwei Bu |
| author_sort | Yangchao He |
| collection | DOAJ |
| description | This paper proposes a trajectory prediction method via the adaptive cost function to address the difficulties in inferring the attack intention and maneuver mode, as well as the accumulation of prediction error during the trajectory prediction of reentry glide vehicles. Firstly, the vehicle guidance task is divided into two distinct categories: conventional guidance and no-fly zone avoidance guidance. A task-matched time-varying parameter prediction model set is then constructed. Secondly, taking into account the maneuverability, guidance intent, and battlefield situation of the vehicle, an adaptive intent cost function adapted to the guidance task is proposed, which avoids the estimation failure problem caused by manually setting cost coefficients in traditional methods. Finally, long-term trajectory prediction of vehicles is achieved using Bayesian theory to infer the attack intent and parametric model with the maximum a posteriori probability. The results of the simulations demonstrate that the proposed prediction method is capable of accurately inferring the vehicle’s attack intention and parameter model, and of effectively reducing the accumulation of prediction errors and the time required for the algorithmic process compared to existing methods. |
| format | Article |
| id | doaj-art-d33ba6dab79d4a8392cf5928c565d730 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-d33ba6dab79d4a8392cf5928c565d7302025-01-24T13:15:41ZengMDPI AGAerospace2226-43102025-01-011216210.3390/aerospace12010062A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost FunctionYangchao He0Jiong Li1Lei Shao2Chijun Zhou3Xiangwei Bu4Graduate School, Air Force Engineering University, Xi’an 710038, ChinaAir Defence and Antimissile School, Air Force Engineering University, Xi’an 710038, ChinaAir Defence and Antimissile School, Air Force Engineering University, Xi’an 710038, ChinaAir Defence and Antimissile School, Air Force Engineering University, Xi’an 710038, ChinaAir Defence and Antimissile School, Air Force Engineering University, Xi’an 710038, ChinaThis paper proposes a trajectory prediction method via the adaptive cost function to address the difficulties in inferring the attack intention and maneuver mode, as well as the accumulation of prediction error during the trajectory prediction of reentry glide vehicles. Firstly, the vehicle guidance task is divided into two distinct categories: conventional guidance and no-fly zone avoidance guidance. A task-matched time-varying parameter prediction model set is then constructed. Secondly, taking into account the maneuverability, guidance intent, and battlefield situation of the vehicle, an adaptive intent cost function adapted to the guidance task is proposed, which avoids the estimation failure problem caused by manually setting cost coefficients in traditional methods. Finally, long-term trajectory prediction of vehicles is achieved using Bayesian theory to infer the attack intent and parametric model with the maximum a posteriori probability. The results of the simulations demonstrate that the proposed prediction method is capable of accurately inferring the vehicle’s attack intention and parameter model, and of effectively reducing the accumulation of prediction errors and the time required for the algorithmic process compared to existing methods.https://www.mdpi.com/2226-4310/12/1/62reentry glide vehiclelong-time trajectory predictionadaptive cost functionattack intent inferenceparameter model inferenceBayesian theory |
| spellingShingle | Yangchao He Jiong Li Lei Shao Chijun Zhou Xiangwei Bu A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost Function Aerospace reentry glide vehicle long-time trajectory prediction adaptive cost function attack intent inference parameter model inference Bayesian theory |
| title | A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost Function |
| title_full | A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost Function |
| title_fullStr | A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost Function |
| title_full_unstemmed | A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost Function |
| title_short | A Trajectory Prediction Method for Reentry Glide Vehicles via Adaptive Cost Function |
| title_sort | trajectory prediction method for reentry glide vehicles via adaptive cost function |
| topic | reentry glide vehicle long-time trajectory prediction adaptive cost function attack intent inference parameter model inference Bayesian theory |
| url | https://www.mdpi.com/2226-4310/12/1/62 |
| work_keys_str_mv | AT yangchaohe atrajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT jiongli atrajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT leishao atrajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT chijunzhou atrajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT xiangweibu atrajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT yangchaohe trajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT jiongli trajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT leishao trajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT chijunzhou trajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction AT xiangweibu trajectorypredictionmethodforreentryglidevehiclesviaadaptivecostfunction |