Uncovering drone intentions using control physics informed machine learning
| dc.contributor.author | Perrusquía, Adolfo | |
| dc.contributor.author | Guo, Weisi | |
| dc.contributor.author | Fraser, Benjamin | |
| dc.contributor.author | Wei, Zhuangkun | |
| dc.contributor.funder | This work was supported by the Engineering and Physical Sciences Research Council under Grant EP/V026763/1 and by the Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme. | |
| dc.date.accessioned | 2024-03-18T11:27:40Z | |
| dc.date.available | 2024-03-18T11:27:40Z | |
| dc.date.issued | 2024-02-24 | |
| dc.description.abstract | Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229. | en_UK |
| dc.identifier.citation | Perrusquía A, Guo W, Fraser B, Wei Z. (2024) Uncovering drone intentions using control physics informed machine learning. Communications Engineering, Volume 3, February 2024, Article number 36 | en_UK |
| dc.identifier.issn | 2731-3395 | |
| dc.identifier.uri | https://doi.org/10.1038/s44172-024-00179-3 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/21018 | |
| dc.language.iso | en_UK | en_UK |
| dc.publisher | Springer Nature | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.title | Uncovering drone intentions using control physics informed machine learning | en_UK |
| dc.type | Article | en_UK |
| dcterms.dateAccepted | 2024-02-07 |