摘要
MEMS传感器中随机误差较大,有时会覆盖传感器中有用信号,提出采用Allan方差(Allan variance)方法对MEMS传感器实测数据进行分析,系统地分析了引起MEMS传感器误差的随机噪声种类及其来源和特性,确定其各项系数,根据系数获得其功率谱密度,根据功率谱密度分析法与Allan方差分析法获得对应各项随机误差的数学模型,然后以数学表达式的形式得到统一的数学模型,再与卡尔曼滤波相结合得到增强的卡尔曼滤波,最后通过车载实验对MEMS-INS/GPS各个姿态进行卡尔曼滤波与改进后卡尔曼滤波2种滤波方法的比较,实验结果表明新滤波方法能很好地提高微惯性系统各个姿态精度。
In some cases, large stochastic errors usually override effective signals in MEMS sensors. Allan variance approach as a common way in analyzing frequency stability in time domain has been proposed to analyze the data measured with MEMS sensors and describe the sources and characteristics of random noises that cause measurement errors of MEMS sensors. The coefficients of individual stochastic error are determined using Allan variance approach to obtain their power spectral densities. Meanwhile, the mathematical models of all stochastic errors are derived in combination with power spectral density analysis. A unified calibration mathematical model is obtained in the form of differential equation for multiple stochastic errors. An enhanced Kalman filter is designed to obtain better filtering effect through incorporating the unified calibration model into traditional MEMS-INS error equation. Finally, traditional and enhanced Kalman filters are applied to measure the poses of MEMS-INS/GPS in vehicle experiments and result shows that enhanced Kalman filter is superior to traditional one in improving the precision of micro inertial navigation.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2011年第12期2863-2868,共6页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(50805004)
模式识别与智能机器人系统学术创新团队项目(PHR201107149)资助项目
作者简介
高宗余，1998年于兰州铁道学院获得学士学位，2005年于兰州交通大学获得硕士学位，2010年于北京工业大学获得博士学位，现为北京联合大学讲师，主要研究方向为MEMS技术、惯导技术及数据融合。E—mail：gzy19750510@163．com方建军，1993年于华中农业大学获得学士学位，1998年于中国农业大学获得博士学位，现为北京联合大学自动化学院教授、院长，主要研究方向为机器人技术、MEMS技术和嵌入式系统应用。E—mail：fangjj1947@126．com
