A Two-Stages Unsupervised/Supervised Statistical Learning Approach for Drone Behaviour Prediction

Singh, Gurpreet; Perrusquía, Adolfo; Guo, Weisi

doi:10.1109/codit58514.2023.10284495

Cited by 6 publications

References 31 publications

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Uncovering drone intentions using control physics informed machine learning

Perrusquía,

Guo,

Fraser

et al. 2024

Commun Eng

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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.

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