Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912606028038144 |
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| author | Aung, Nyi Nyi Muralles, Neil Stein, Adrian |
| author_facet | Aung, Nyi Nyi Muralles, Neil Stein, Adrian |
| contents | This work addresses object identification under known dynamics in unmanned aerial vehicle applications, where learning and classification are combined through a physics-informed residual neural network. The proposed framework leverages physics-informed learning for state mapping and state-derivative prediction, while a softmax layer enables multi-class confidence estimation. Quadcopter, fixed-wing, and helicopter aerial vehicles are considered as case studies. The results demonstrate high classification accuracy with reduced training time, offering a promising solution for system identification problems in domains where the underlying dynamics are well understood. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21405 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification Aung, Nyi Nyi Muralles, Neil Stein, Adrian Machine Learning Robotics This work addresses object identification under known dynamics in unmanned aerial vehicle applications, where learning and classification are combined through a physics-informed residual neural network. The proposed framework leverages physics-informed learning for state mapping and state-derivative prediction, while a softmax layer enables multi-class confidence estimation. Quadcopter, fixed-wing, and helicopter aerial vehicles are considered as case studies. The results demonstrate high classification accuracy with reduced training time, offering a promising solution for system identification problems in domains where the underlying dynamics are well understood. |
| title | Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2509.21405 |