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.
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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