基于卡尔曼滤波的陀螺仪阵列技术研究

doi:10.3969/j.issn.1000-1093.2016.02.012

兵工学报 ›› 2016, Vol. 37 ›› Issue (2): 272-278.doi: 10.3969/j.issn.1000-1093.2016.02.012

基于卡尔曼滤波的陀螺仪阵列技术研究

刘明雍，朱立，董海霞

（西北工业大学航海学院，陕西西安 710072）

收稿日期:2015-06-23 修回日期:2015-06-23 上线日期:2016-04-22
作者简介:刘明雍（1971—），男，教授，博士生导师
基金资助:
国家自然科学基金项目（51179156、51379136）

Research on Gyroscope Array Based on Kalman Filter

LIU Ming-yong, ZHU Li, DONG Hai-xia

(School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China)

Received:2015-06-23 Revised:2015-06-23 Online:2016-04-22

摘要/Abstract

摘要： 多个相同型号的陀螺仪测量轴相互平行，测量同一个角速度信号所组成的阵列叫做陀螺阵列。通过研究陀螺阵列提高惯性测量精度的信息处理算法，建立单个微机电（MEMS）陀螺仪的两种不同漂移模型，利用Allan方差对漂移系数进行辨识，将辨识出的随机漂移系数应用于卡尔曼滤波。通过卡尔曼滤波将陀螺阵列的信息融合为一个较高精度的输出，证明了卡尔曼滤波的稳定性。通过实验对比了不同建模方法的优劣，并且验证了基于卡尔曼滤波的信息融合方法可以有效提高MEMS陀螺仪的精度。

关键词: 控制科学与技术, 陀螺仪阵列, 信息融合, 卡尔曼滤波

Abstract: The same type of measurement axes of gyroscope are parallel to each other. The information processing algorithms to improve the accuracy of inertial measurement via gyroscope array are researched. Two different drift models of single MEMS gyroscope are established, and the random drift coefficients are identified via Allan variance. Then the identified random drift coefficients are used for Kalman filter, and the muti-gyroscopes information is fused to acquire a highly precise output of gyroscope array through Kalman filter. The result proves the stability of Kalman filter. Finally, the advantages and disadvantages of different modeling methods are compared by experiment. The experimental results show that the accuracy of MEMS gyroscope can be improved effectively by the information fusion method based on Kalman filter.

Key words: control science and technology, gyroscope array, information fusion, Kalman filter

中图分类号:

TP14

刘明雍，朱立，董海霞. 基于卡尔曼滤波的陀螺仪阵列技术研究[J]. 兵工学报, 2016, 37(2): 272-278.

LIU Ming-yong, ZHU Li, DONG Hai-xia. Research on Gyroscope Array Based on Kalman Filter[J]. Acta Armamentarii, 2016, 37(2): 272-278.

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基于卡尔曼滤波的陀螺仪阵列技术研究

Research on Gyroscope Array Based on Kalman Filter

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