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Effective nested Kalman fusion for improving microelectromechanical system array performance
Measurement Science and Technology ( IF 2.4 ) Pub Date : 2020-09-09 , DOI: 10.1088/1361-6501/aba323
Siyuan Liang , Yijie Huang , Weilong Zhu , Feng Zhao

Microelectromechanical systems (MEMS) are widely used in the navigation field due to their low cost and easy integration. Its low positioning accuracy restricts its expansion into the high-end navigation field. To improve the performance of MEMS inertial devices, this paper proposes a nested Kalman fusion (NKF) for MEMS gyroscope array data fusion applied to the virtual gyroscope. First, the algorithm processes the raw gyroscope array data through Kalman filtering. Secondly, the obtained filtered array data converge as a virtual gyroscope by support degree data fusion—the NKF experimental data collected by the actual test. The experimental results show the zero-bias instability, angular random walk and rate ramp of the original data are improved by 10.64 dB, 12.45 dB and 10.26 dB, respectively, by the NKF algorithm. NKF can adjust the gyro parameters by about 6 dB in comparison with existing MEMS optimization algorithms.

中文翻译:

有效的嵌套Kalman融合以改善微机电系统阵列性能

由于其低成本和易于集成,微机电系统(MEMS)被广泛用于导航领域。它的低定位精度限制了它向高端导航领域的扩展。为了提高MEMS惯性设备的性能,本文提出了一种应用于虚拟陀螺仪的MEMS陀螺仪阵列数据融合的嵌套Kalman融合(NKF)。首先,该算法通过卡尔曼滤波处理原始陀螺仪阵列数据。其次,通过支持度数据融合(通过实际测试收集的NKF实验数据)将获得的滤波后的阵列数据收敛为虚拟陀螺仪。实验结果表明,通过NKF算法,原始数据的零偏置不稳定性,角度随机游走和速率斜率分别提高了10.64 dB,12.45 dB和10.26 dB。
更新日期:2020-09-10
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