摘要
海上船舶的精确跟踪和实时航迹预测是避免船只碰撞、实现海上交通安全管理的关键技术。传统卡尔曼滤波模型中存在系统缺陷,其要求噪声模型为先验知识,不符合实际应用情况。为弥补此不足,本文以Mehra自适应滤波思路为基础,提出一种自适应卡尔曼滤波算法,以动态统计的形式体现噪声模型的变化特性,并通过在线自适应调整噪声均值和协方差,及时修正滤波预测结果,提高船舶跟踪精度。最后通过仿真实验证明该算法的有效性。
The accurate tracking and real-time prediction of marine vessels are the key technologies to avoid the collision of ships and to realize the management of maritime traffic safety. Aiming at the defects of the prior knowledge of the noise model in the traditional Kalman filter structure,we propose an adaptive Kalman filtering algorithm based on the idea of Mehra adaptive filter. Through the dynamic statistical noise characteristics,we can adjust the mean and covariance of the noise on line to realize the high precision for the ship tracking. Finally,we show the effectiveness of the algorithm through simulation experiments.
出处
《舰船科学技术》
北大核心
2016年第11X期52-54,共3页
Ship Science and Technology
