As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements.

随着自主船舶成为海上运输的未来趋势，开发智能自主导航系统以确保船舶航行安全至关重要。在系统的三个核心组件（传感，计划和控制模块）中，准确检测目标船的导航信息至关重要。在典型的海洋环境中，传感器噪声的存在以及环境条件变化产生的影响在很大程度上限制了使用单个传感器进行环境感知的可靠性。因此，至关重要的是将多个传感器与多传感器数据融合技术一起使用以提高检测性能。在本文中，提出并设计了一种基于模糊逻辑的运动目标舰船检测多传感器数据融合算法。特别开发了一种两阶段模糊逻辑关联方法，并将其与Kalman滤波集成在一起，以实现计算高效的性能。该算法的有效性已经在模拟中验证和验证，在该模拟中，多个目标船只正以复杂的运动进行航行。