When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel’s structure, are to be considered.

在考虑无人水面航行器 (USV) 运行的工作条件时，本来就具有不确定性的导航传感器会受到可能影响 USV 导航的准确性、安全性和可靠性的环境影响。为了解决这个问题，本文将开发多传感器数据融合算法来处理来自三种常用传感器的原始传感器测量值，并为 USV 在实际环境中的运行计算改进的导航数据。无迹卡尔曼滤波器作为一种专用于处理非线性系统的高级滤波技术，已被用作底层算法，其性能在各种基于计算机的模拟中得到验证，其中实际的动态导航影响，例如洋流，