电子学报 ›› 2014, Vol. 42 ›› Issue (3): 512-516.DOI: 10.3969/j.iss.0372-2012-2014.03.014

• 学术论文 • 上一篇    下一篇

船舶动力定位多传感器闭环分级融合算法

徐树生1, 林孝工2   

  1. 1. 青岛农业大学机电工程学院, 山东青岛 266109;
    2. 哈尔滨工程大学自动化学院, 黑龙江哈尔滨 150001
  • 收稿日期:2010-10-12 修回日期:2013-10-15 出版日期:2014-03-25 发布日期:2014-03-25
  • 作者简介:徐树生 男,1966年4月出生于山东淄博.2013年在哈尔滨工程大学获工学博士学位.现为青岛农业大学机电工程学院副教授,主要研究方向为水下仿生机器人、信号处理、多传感器数据融合等.E-mail:xuss0319@163.com
  • 基金资助:

    国家自然科学基金(No.60775060)

A Closed-Loop Hierarchical Multi-Sensor Fusion Algorithm for Vessel Dynamic Positioning

XU Shu-sheng1, LIN Xiao-gong2   

  1. 1. College of Electromechanical Engineering, Qingdao Agricultural University, Qingdao, Shandong 266109, China;
    2. College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
  • Received:2010-10-12 Revised:2013-10-15 Online:2014-03-25 Published:2014-03-25

摘要: 为了改善船舶动力定位多传感器的融合性能,提出了一种多传感器闭环分级融合算法.该算法包括基于卡尔曼滤波的局部和全局两级估计,以全局融合信息的反馈作为局部估计的初始值进行时间更新,定义两级估计的映射关系并引入调节系数,自适应地调节全局融合增益.全局估计融入各局部估计过程中,两级估计组成了一个相互补偿的闭环系统.利用船舶半实物仿真系统的试验,仿真验证了该算法的有效性.

关键词: 多传感器融合, 状态估计, 分级融合, 卡尔曼滤波

Abstract: To improve the multi-sensor fusion performance of vessel dynamic positioning,a closed-loop hierarchical fusion algorithm is proposed.The presented fusion algorithm has local and global estimators.The Kalman filter is used in both the local and global estimates.The global fusion is fed back to the local filters.Mapping relationships between the local and global estimates are defined by environment conditions derived from each local estimator.Adjusting factors based on the local and global estimates covariances are introduced.The gain of the global fusion is adjusted optimally according to the mapping matrices and adjusting factors.The global estimate is included in local estimates.The proposed algorithm combines the local and global estimates into one closed-loop mutual compensation system.By the vessel semi-physical simulation system the validities of the proposed algorithm are verified.

Key words: multi-sensor fusion, state estimate, hierarchical fusion, Kalman filter

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