Kui Zhu ¹ Xueyao Chen ^1* Lin Mu ^2* Dingfeng Yu ^3* Runze Yu ^2* Zhaolong Sun ^1* Tong Zhou ^3*

• ¹ Naval University of Engineering, Wuhan, China
• ² China University of Geosciences Wuhan, Wuhan, Hubei Province, China
• ³ Wuhan Second Ship Design and Research Institute, Wuhan, Hubei Province, China

The motion of waves in water causes the slow movement of drifting sea targets-a phenomenon usually ignored in target-drift prediction models for maritime search and rescue (SAR). This study examined the wave-induced drift's influence on field-observation experiments involving two common, differently sized SAR targets-an offshore fishing vessel (OFV) and a person in the water (PIW)-using parameter stepwise calibration and machine-learning (ML) methods. The sample of wave-induced drift velocity was obtained by gradually separating current-induced (CI) drift's and wind-induced (WI) drift's influence from the target-drift velocity using the least-square method and AP98 model. A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. Finally, the drift trajectories considering the influence of waves were fitted and verified based on 2 independent samples respectively.Compared with the force analysis method, the accuracy of the ML methods in the verification test was higher. In addition, the results show that for OFVs, considering wave-induced drift's influence in the ensemble-trajectory prediction could improve the simulation accuracy. However, for a PIW, no significant improvement was observed. This result also indicates that wave-induced drift may not be simply ignored in large SAR targets' drift prediction.