###### Abstract
This method can realize the real-time estimation and effective compensation of the navigation system, and can resist the influence of external environment disturbing forces such as currents and effectively improve the anti-interference performance of the system. Compared with the classical PID control, this method can better meet the requirements of the navigation system for high-precision control performance. [PERSON] et al. [15] proposed a dynamic surface inversion trajectory tracking control method for underactuated UUV to solve the trajectory tracking control problem of unmanned underwater vehicles with unknown current disturbances. An improved current disturbance observer is designed to estimate the unknown current disturbance in the dynamic model of the vehicle. The results show that the designed observer can effectively deal with the time-varying current interference problem and accurately complete the trajectory tracking control task of UUV. The above research provides an effective method reference for the drift motion of the AUV under the interaction of ocean parameters such as current and wave. What is more noteworthy is that the drift motion of the AUV after the crash has strong time characteristics in addition to the motion characteristics under the interaction of current, wave, and other factors. That is to say, the predicted position of the AUV at the current moment belongs to the multivariate time series prediction problem which depends on the position of the previous moment and the common influence of the ocean environment at this moment. However, the ocean situation is complex and there is a phenomenon of ocean stratification [16]. Ocean stratification refers to the hierarchical structure of thermodynamic state parameters such as density,temperature, and salinity of seawater with depth. According to the hierarchical structure, the ocean is mainly divided into thermocline layer and deep ocean layer (homogeneous layer), in which the ocean parameters of drift movement caused by interaction with AUV are also different. When the AUV drifts in the deep ocean layer, the drift movement of the AUV is mainly affected by tidal current and density current. When the AUV drifts to the thermocline [17], a small volume will be exposed above the water surface and continue to drift on the ocean surface under the action of residual buoyancy [18]. The drift direction is mainly affected by factors such as ocean surface wind, current, wave, AUV structure shape, and water attitude. At the same time, the ocean surface current and wave are very complex, and they also need to be solved and predicted based on the historical data of ocean information and the current ocean information data. As a result, the time series model prediction results will deviate from the correct track sequence when in the different ocean layers, and the error will accumulate over time, which results in a decrease in the accuracy of the model. Aiming at the problems in the above AUV drift prediction, a new method for predicting AUV drift trajectory is proposed. Inspired by the emotion modulation mechanism of cognitive psychology, a modified neural network is proposed and established. The prediction deviation of different ocean layers is corrected in real time by the two network modules of Proactive Modulation and Response Modulation to help improve the real-time accuracy of the AUV drift track prediction.