| 异质双9轴MEMS惯性传感器数据互补-加权迭代融合算法 |
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| 引用本文： | 王伟,张金艺,张洪辉,蔡春艳,李建宇.异质双9轴MEMS惯性传感器数据互补-加权迭代融合算法[J].应用科学学报,2015,33(5):491-501. |
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| 作者姓名： | 王伟 张金艺 张洪辉 蔡春艳 李建宇 |
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| 作者单位： | 1. 上海大学特种光纤与光接入网省部共建教育部重点实验室, 上海 200072;
2. 上海大学微电子研究与开发中心, 上海 200072;
3. 上海大学教育部新型显示与系统应用重点实验室, 上海 200072 |
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| 基金项目： | 国家"863"高技术研究发展计划基金(No.2013AA03A1121, No.2013AA03A1122);上海市教委重点学科基金(No.J50104)资助 |
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| 摘 要： | 在MEMS惯性传感器导航研究中,传统的MEMS捷联式惯性导航系统仅利用单多轴MEMS惯性传感器对移动目标进行导航定位,其测量值和噪声特性易受环境影响,此外加速度误差、陀螺仪漂移、平台框架角误差、平台安装误差等因素也严重影响传感器性能. 为此,从异质非单多轴MEMS惯性传感器互补融合角度出发,用异质双9轴MEMS惯性传感器采集移动目标原始信息,并提出互补-加权迭代融合算法. 首先对异质双9轴MEMS惯性传感器测得的原始数据进行预处理,基于卡尔曼滤波用最小方差估计法求解观测值. 通过估值方差和革新方程形成权值更新模型,实现异质双9轴MEMS惯性传感器数据的互补融合. 实验表明,相较传统单9轴MEMS惯性传感器导航,该算法可提高导航精度50%以上.
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| 关 键 词： | 导航 MEMS 数据融合 卡尔曼滤波器 |
| 收稿时间： | 2015-05-17 |
| 修稿时间： | 2015-06-29 |
Complementary-Weighted Iterative Fusion Algorithm for Heterogeneous Dual 9-Axis MEMS Inertial Sensor Data |
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| WANG Wei,ZHANG Jin-yi,ZHANG Hong-hui,CAI Chun-yan,LI Jian-yu.Complementary-Weighted Iterative Fusion Algorithm for Heterogeneous Dual 9-Axis MEMS Inertial Sensor Data[J].Journal of Applied Sciences,2015,33(5):491-501. |
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| Authors: | WANG Wei ZHANG Jin-yi ZHANG Hong-hui CAI Chun-yan LI Jian-yu |
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| Institution: | 1. Key Laboratory of Special Fiber Optics and Optical Access Networks, Ministry of Education, Shanghai University, Shanghai 200072, China;
2. Microelectronic Research and Development Center, Shanghai University, Shanghai 200072, China;
3. Key Laboratory of Advanced Displays and System Application, Ministry of Education, Shanghai University, Shanghai 200072, China |
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| Abstract: | In micro-electro-mechanical system (MEMS) inertial sensor navigation, traditional techniques only use single multi-axis sensor to navigate and get position of the moving target. However, measurements of sensors and noise characteristics are affected by environment conditions. Besides, acceleration error, gyro drift, platform angle error and the error of platform installation also have important influences. From the view point of data fusion of complementary non-single multi-axis sensor, this paper uses two 9-axis MEMS inertial sensors to collect the original information of moving targets, and develops a complementary-weighted iterative fusion algorithm. The raw data of heterogeneous dual 9-axis MEMS inertial sensor are preprocessed. Then, using minimum variance, the observed values are estimated with Kalman filtering. A model of weight updating is established to improve accuracy according to evaluation of variance and an innovation equation. Experimental results show that, comparing with the traditional method, the proposed algorithm can improve navigation accuracy by more than 50%. |
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| Keywords: | navigation micro-electro-mechanical system (MEMS) data fusion Kalman filtering |
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