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题名: 基于加速度信号增强的无色卡尔曼滤波方法在水面移动机器人中的应用
其他题名: Acceleration Enhanced Unscented Kalman Filter Algorithm and Its Applications on Unmanned Surface Vehicles
作者: 马玉龙 ; 何玉庆 ; 韩建达 ; 赵忆文
作者部门: 机器人学研究室
关键词: 加速度 ; 无色Kalman滤波 ; 计算复杂度 ; 状态估计
刊名: 机械工程学报
ISSN号: 0577-6686
出版日期: 2013
卷号: 49, 期号:1, 页码:31-38
收录类别: EI ; CSCD
产权排序: 1
摘要: 水面移动机器人系统的高性能航迹跟踪控制要求能够获取高精度的运动状态和不确定性信息(包括内部不确定参数和外部干扰),而直接高精度测量手段的匮乏,以及数学模型的强非线性、祸合性使得如何得到这些信息存在着种种困难。针对此问题,提出利用一种结合基于奇异值分解无色卡尔曼滤波(Singular value decomposition unscented Kalman filter, VDUKF) 算法和加速度测量的新估计算法。SVDUKF 方法是无色卡尔曼滤波(Unscented Kalman filter, UKF)的一种改进方法,具有更宽松的使用条件。此外,该方法的最大优点在于将UKF 算法处理系统强非线性和加速度信号富含扰动信息并可简化系统估计模型等特点结合起来,从而获得了一种精度更高、计算复杂度更低的在线估计算法。从推导水面移动机器人系统非线性模型开始,简要介绍加速度信号对模型的简化原理以及SVDUKF 算法的基本步骤,并通过仿真验证了算法在估计精度和计算效率方面的优越性。
英文摘要: High-performance control of unmanned surface vehicle (USV) trajectory tracking requires the systems to obtain motion state and uncertainty with high precision. However, the lack of direct highly precise measuring methods and the strong nonlinearity and coupling make it difficult to obtain the information needed. As to solve this problem, a kind of novel estimation algorithm is proposed, which combines singular value decomposition unscented Kalman filter (SVDUKF) with acceleration measurement together. SVDUKF is an improved algorithm of unscented Kalman filter (UKF) with wider application conditions. In addition, its main advantage lies in binding the ability of UKF to deal the strong nonlinearity with the feather of acceleration with much disturbance information to simplify the system estimation model so that it becomes an online estimation algorithm with higher precision and lower calculation complexity. The nonlinear model of USV is derived, and the model simplification idea of acceleration and the basic steps of SVDUKF are introduced. The advantages in estimation accuracy and computation efficiency of the proposed algorithm are verified.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/10345
Appears in Collections:机器人学研究室_期刊论文

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