Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification

Document Type

Conference Proceeding

Publication Date

1-1-2025

Abstract

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.

Publication Source (Journal or Book title)

Proceedings 2025 24th International Conference on Machine Learning and Applications Icmla 2025

First Page

115

Last Page

122

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