  Unmanned Surface Vehicles (USVs) are pivotal in marine exploration, but their
sensors' accuracy is compromised by the dynamic marine environment. Traditional
calibration methods fall short in these conditions. This paper introduces a
deep learning architecture that predicts changes in the USV's dynamic
metacenter and refines sensors' extrinsic parameters in real time using a
Time-Sequence General Regression Neural Network (GRNN) with Euler angles as
input. Simulation data from Unity3D ensures robust training and testing.
Experimental results show that the Time-Sequence GRNN achieves the lowest mean
squared error (MSE) loss, outperforming traditional neural networks. This
method significantly enhances sensor calibration for USVs, promising improved
data accuracy in challenging maritime conditions. Future work will refine the
network and validate results with real-world data.
