Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles
In response to the increasing threat of hypersonic weapons, it is of great importance for the defensive side to achieve fast prediction of their feasible attack domain and online inference of their most probable targets. In this study, an online footprint prediction and attack intention inference al...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/1/185 |
| _version_ | 1797406778429800448 |
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| author | Jingjing Xu Changhong Dong Lin Cheng |
| author_facet | Jingjing Xu Changhong Dong Lin Cheng |
| author_sort | Jingjing Xu |
| collection | DOAJ |
| description | In response to the increasing threat of hypersonic weapons, it is of great importance for the defensive side to achieve fast prediction of their feasible attack domain and online inference of their most probable targets. In this study, an online footprint prediction and attack intention inference algorithm for hypersonic glide vehicles (HGVs) is proposed by leveraging the utilization of deep neural networks (DNNs). Specifically, this study focuses on the following three contributions. First, a baseline multi-constrained entry guidance algorithm is developed based on a compound bank angle corridor, and then a dataset containing enough trajectories for the following DNN learning is generated offline by traversing different initial states and control commands. Second, DNNs are developed to learn the functional relationship between the flight state/command and the corresponding ranges; on this basis, an online footprint prediction algorithm is developed by traversing the maximum/minimum ranges and different heading angles. Due to the substitution of DNNs for multiple times of trajectory integration, the computational efficiency for footprint prediction is significantly improved to the millisecond level. Third, combined with the predicted footprint and the hidden information in historical flight data, the attack intention and most probable targets can be further inferred. Simulations are conducted through comparing with the state-of-the-art algorithms, and results demonstrate that the proposed algorithm can achieve accurate prediction for flight footprint and attack intention while possessing significant real-time advantage. |
| first_indexed | 2024-03-09T03:30:33Z |
| format | Article |
| id | doaj.art-76292513a9c84e898a6ce584e4b23d92 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T03:30:33Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-76292513a9c84e898a6ce584e4b23d922023-12-03T14:55:33ZengMDPI AGMathematics2227-73902022-12-0111118510.3390/math11010185Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide VehiclesJingjing Xu0Changhong Dong1Lin Cheng2School of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaIn response to the increasing threat of hypersonic weapons, it is of great importance for the defensive side to achieve fast prediction of their feasible attack domain and online inference of their most probable targets. In this study, an online footprint prediction and attack intention inference algorithm for hypersonic glide vehicles (HGVs) is proposed by leveraging the utilization of deep neural networks (DNNs). Specifically, this study focuses on the following three contributions. First, a baseline multi-constrained entry guidance algorithm is developed based on a compound bank angle corridor, and then a dataset containing enough trajectories for the following DNN learning is generated offline by traversing different initial states and control commands. Second, DNNs are developed to learn the functional relationship between the flight state/command and the corresponding ranges; on this basis, an online footprint prediction algorithm is developed by traversing the maximum/minimum ranges and different heading angles. Due to the substitution of DNNs for multiple times of trajectory integration, the computational efficiency for footprint prediction is significantly improved to the millisecond level. Third, combined with the predicted footprint and the hidden information in historical flight data, the attack intention and most probable targets can be further inferred. Simulations are conducted through comparing with the state-of-the-art algorithms, and results demonstrate that the proposed algorithm can achieve accurate prediction for flight footprint and attack intention while possessing significant real-time advantage.https://www.mdpi.com/2227-7390/11/1/185reentry guidancefootprint predictionattack intention inferencedeep neural network |
| spellingShingle | Jingjing Xu Changhong Dong Lin Cheng Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles Mathematics reentry guidance footprint prediction attack intention inference deep neural network |
| title | Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles |
| title_full | Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles |
| title_fullStr | Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles |
| title_full_unstemmed | Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles |
| title_short | Deep Neural Network-Based Footprint Prediction and Attack Intention Inference of Hypersonic Glide Vehicles |
| title_sort | deep neural network based footprint prediction and attack intention inference of hypersonic glide vehicles |
| topic | reentry guidance footprint prediction attack intention inference deep neural network |
| url | https://www.mdpi.com/2227-7390/11/1/185 |
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