Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models
This study investigates high-performance models for predicting the Weapon Engagement Zone (WEZ) in beyond-visual-range (BVR) air combat scenarios. Accurate WEZ predictions are crucial for decision-making in air combat, and high-performance solutions are essential for developing and deploying autonom...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856150/ |
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| author | Andre R. Kuroswiski Annie S. Wu Angelo Passaro |
| author_facet | Andre R. Kuroswiski Annie S. Wu Angelo Passaro |
| author_sort | Andre R. Kuroswiski |
| collection | DOAJ |
| description | This study investigates high-performance models for predicting the Weapon Engagement Zone (WEZ) in beyond-visual-range (BVR) air combat scenarios. Accurate WEZ predictions are crucial for decision-making in air combat, and high-performance solutions are essential for developing and deploying autonomous systems. To optimize the model training process, we introduce novel feature engineering and data augmentation strategies, achieving a 70% improvement in the Mean Absolute Error (MAE) of WEZ predictions. A comparison of various regression methods highlights the potential of polynomial-based alternatives when fully utilized. In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. For instance, Lasso regression, a PR method with regularization, achieves results that are 33% better and 2.1 times faster than the best artificial neural network-based solution. Our results challenge common assumptions in the literature about the complexity and feasibility of higher-order PR solutions, suggesting that they can be a compelling alternative for various challenges across domains. This study also provides a new open dataset to facilitate further research and advancements in this field. |
| format | Article |
| id | doaj-art-3ae10a22de9c42d89f82ee266301f5c3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3ae10a22de9c42d89f82ee266301f5c32025-02-06T00:00:15ZengIEEEIEEE Access2169-35362025-01-0113217592177210.1109/ACCESS.2025.353555510856150Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression ModelsAndre R. Kuroswiski0https://orcid.org/0000-0003-1549-2434Annie S. Wu1Angelo Passaro2https://orcid.org/0000-0002-2421-0657Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, BrazilDepartment of Computer Science, University of Central Florida, Orlando, FL, USAInstituto de Estudos Avançados, São José dos Campos, São Paulo, BrazilThis study investigates high-performance models for predicting the Weapon Engagement Zone (WEZ) in beyond-visual-range (BVR) air combat scenarios. Accurate WEZ predictions are crucial for decision-making in air combat, and high-performance solutions are essential for developing and deploying autonomous systems. To optimize the model training process, we introduce novel feature engineering and data augmentation strategies, achieving a 70% improvement in the Mean Absolute Error (MAE) of WEZ predictions. A comparison of various regression methods highlights the potential of polynomial-based alternatives when fully utilized. In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. For instance, Lasso regression, a PR method with regularization, achieves results that are 33% better and 2.1 times faster than the best artificial neural network-based solution. Our results challenge common assumptions in the literature about the complexity and feasibility of higher-order PR solutions, suggesting that they can be a compelling alternative for various challenges across domains. This study also provides a new open dataset to facilitate further research and advancements in this field.https://ieeexplore.ieee.org/document/10856150/Air combatlasso regressionpolynomial regressionridge regressionweapon engagement zone |
| spellingShingle | Andre R. Kuroswiski Annie S. Wu Angelo Passaro Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models IEEE Access Air combat lasso regression polynomial regression ridge regression weapon engagement zone |
| title | Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models |
| title_full | Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models |
| title_fullStr | Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models |
| title_full_unstemmed | Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models |
| title_short | Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models |
| title_sort | optimized prediction of weapon effectiveness in bvr air combat scenarios using enhanced regression models |
| topic | Air combat lasso regression polynomial regression ridge regression weapon engagement zone |
| url | https://ieeexplore.ieee.org/document/10856150/ |
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