Study on Field Data Fusion and Calibration Techniques of MEMS Array
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摘要: 针对微电子机械系统(MEMS)陀螺随机噪声大及零偏重复性差的特点, 对MEMS随机误差采用Allan方差分析其角度随机游走, 以Allan方差辨识值和加权最小二乘法设计阵列陀螺信息融合处理算法, 可有效降低角度随机游走, 且在静态和动态条件下均能实时响应真实角速率。对MEMS陀螺常值漂移, 结合惯性导航系统误差的可观测性设计两位置标定方案, 完成常值漂移的系统级标定。仿真结果表明, 文中所提方法可有效降低MEMS陀螺角度随机游走和陀螺常值漂移, 显著提升MEMS惯性测量精度。Abstract: Due to the poor bias repeatability and large random noise of a micro electro mechanical system (MEMS), the Allan variance was used to analyze the random angle walk of MEMS. The information fusion algorithm of array gyro was designed by using Allan variance identification value and weighted least square method, which could effectively reduce the random angle walk and respond to the true angular rate in real time under both static and dynamic conditions. For the constant drift of MEMS gyro, a two-position calibration scheme was designed combined with the observability of the error of the inertial navigation system, so as to complete system-level calibration of constant drift. Simulation results show that the method proposed in this paper effectively reduces the random angle walk and the constant drift of MEMS gyro and significantly improves the inertial measurement accuracy of MEMS.
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Key words:
- micro electro mechanical system /
- array /
- Allan variance /
- system-level calibration /
- information fusion
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