Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification
This provides a solution for system identification in UAV applications where dynamics are known, but it is incremental as it adapts existing physics-informed methods to a specific domain.
The paper tackles object identification for UAVs by combining physics-informed learning with a residual neural network, achieving high classification accuracy and reduced training time for quadcopter, fixed-wing, and helicopter vehicles.
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.