To solve the trajectory tracking problem of Unmanned Surface Vessels (USV) in complex external environment, a control algorithm combining Adaptive Unscented Kalman Filter (AUKF) with Nonlinear Model Predictive Control (NMPC) is proposed. Firstly, the mathematical model of the 3-DOF fully-driven USV is established, and a nonlinear model predictive controller with dynamic weight adjustment based on current tracking error is designed. Secondly, according to the characteristics of complex environment and uncertain sensor noise, an AUKF is proposed to estimate the state by correlating noise changes with measurement parameters. Finally, by adding time-varying random disturbance and measurement noise to simulate the real external environment for the numerical simulation experiment, the certainty and effectiveness of the algorithm are verified.