[1]韦黔[][],陈迪[] [],林树靖[][],等. 基于迭代卡尔曼滤波的传感器数据融合仿真[J].计算机技术与发展,2017,27(09):137-140.
　WEI Qian[][],CHEN Di[][],LIN Shu-jing[] [],et al. Simulation of Multisensor Data Fusion Based on Iterative Kalman Filter[J].,2017,27(09):137-140.
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 基于迭代卡尔曼滤波的传感器数据融合仿真()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年09期
页码:
137-140
栏目:
应用开发研究
出版日期:
2017-09-10

文章信息/Info

Title:
 Simulation of Multisensor Data Fusion Based on Iterative Kalman Filter
文章编号:
1673-629X（2017)09-0137-04
作者:
 韦黔[1][2];  陈迪[1] [2]; 林树靖[1][2] ; 仇三铭[1] [2]; 张同兴[3]
 1.上海交通大学 电子信息与电气工程学院 仪器科学与工程系；2.上海智能诊疗仪器工程技术研究中心；3.上海直川电子科技有限公司
Author(s):
 WEI Qian[1][2]; CHEN Di[1][2] ; LIN Shu-jing[1] [2]; QIU San-ming[1][2] ; ZHANG Tong-xing[3]
关键词:
 ; 卡尔曼滤波器; 传感器数据融合; 滤波精度; 迭代法; 仿真
Keywords:
 ; Kalman filter; sensory data fusion; filtering precision; iterative algorithm; simulation
分类号:
TP301.6
文献标志码:
A
摘要:
 在对运动系统进行实时动态姿态信息检测过程中,常常需要加速度计、陀螺仪等多个惯性传感器的协同工作,因此需要对多传感器的输出数据进行融合处理.卡尔曼滤波法是其中常用的一种数据融合算法.该算法的滤波融合精度,直接影响了运动系统姿态信息的精度与实时性.在研究传统的卡尔曼滤波算法的基础上,仿真分析了过程噪声协方差矩阵、测量噪声协方差矩阵对于卡尔曼滤波算法的滤波精度、响应时间的影响,提出了一种基于数据迭代法的卡尔曼滤波融合算法.该算法将多传感器数据融合后的输出再次进行迭代运算,实现了较好的融合效果.仿真实验结果表明,相较于传统的卡尔曼滤波算法,所提出算法的复杂度低,实时性好,同时滤波精度大幅提升.
Abstract:
 It is needed the cooperation for inertial sensors like accelerometers and gyroscopes in real-time and dynamic attitude informa-tion detection for mobile system,so the output data of multisensors is required to fuse and process. The Kalman filter,as a data fusion al-gorithm,is usually adopted and its filtering accuracy would have a direct influence on the accuracy and real-time performance of the atti-tude information of mobile system. On the basis of research on the traditional Kalman filter, simulation and analysis of the impact of process and measurement noise covariance matrix on the accuracy and response time of Kalman filtering algorithm,a novel Kalman filter based on a data iterative algorithm is presented,which carries on the iterative operation again for the output after fusion of multisensors data,realizing the good effect. Simulation shows that it has a good real-time performance and low complexity and promotes filtering ac-curacy significantly compared with traditional Kalman filter.

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更新日期/Last Update: 2017-10-24