基于时延数据融合的港口船舶监控策略研究

在对现有港口船舶航行监控方法以及导航技术详细分析的基础上,结合基于Kalman滤波的数据融合技术,讨论了利用雷达、GPS以及AIS数据进行港口船舶导航算法和监控策略的设计问题。针对现有的时延平滑估计存在的缺点,提出一种基于时延的集中式预测估计融合算法。新算法具有很高的实时性和可实施性,同时,将新算法与现有的时延平滑估计融合算法相集成,提出一种完整的船舶导航和监控策略,计算机仿真算例验证了新算法的优越性,同时显示了新监控策略的有效性。

Study of Harbor Ship Monitoring Strategy Based on Delayed Data Fusion

Based on the analysis of current harbor ship monitoring methods and navigation technology,combined with the data fusion technology based on Kalman filter,the design of ship navigation algorithm and monitoring strategy applying data from radar,GPS and AIS is studied in this paper.Aimed at the defects existing in current delayed smooth fusion estimation method,a centralized prediction estimation fusion algorithm is presented.Compared with current smooth estimate method,this method has higher real-time performance and applicability,a complete strategy of ship navigation and monitoring is presented by integration of the new and current delayed smooth estimate algorithms.Computer simulation examples validate the superiority of the new algorithm and also the effectiveness of new monitoring strategy.