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基于阵列式消费级MEMS信息融合的冗余惯性测量单元无外设校准方法

Peripheral-Free Calibration Method for Redundant IMUs Based on Array-Based Consumer-Grade MEMS Information Fusion.

作者信息

Liang Siyuan, Dong Xiaochao, Guo Tianyu, Zhao Feng, Zhang Yuhua

机构信息

School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710119, China.

School of Computer Science and Technology, Baoji College of Arts and Science, Baoji 721016, China.

出版信息

Micromachines (Basel). 2022 Jul 29;13(8):1214. doi: 10.3390/mi13081214.

DOI:10.3390/mi13081214
PMID:36014135
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9415369/
Abstract

The MEMS array-based inertial navigation module (M-IMU) reduces the measurement singularities of MEMS sensors by fusing multiple data processing to improve its navigation performance. However, there are still existing random and fixed errors in M-IMU navigation. The calibration method calibrates the fixed error parameters of M-IMU to further improve navigation accuracy. In this paper, we propose a low-cost and efficient calibration method to effectively estimate the fixed error parameters of M-IMU. Firstly, we manually rotate the M-IMU in multiple sets of different attitudes (stationary), then use the LM-calibration algorithm to optimize the cost function of the corresponding sensors in different intervals of the stationary-dynamic filter separation to obtain the fixed error parameters of MEMS, and finally, the global fixed error parameters of the M-IMU are calibrated by adaptive support fusion of the individual MEMS fixed error parameters based on the benchmark conversion. A comparison of the MEMS calibrated separately by the fusion-calibration algorithm and the LM-calibration algorithm verified that the calibrated MEMS array improved the measurement accuracy by about 10 db and reduced the dispersion of the output data by about 8 db compared to the individual MEMS in a multi-dimensional test environment, indicating the robustness and feasibility of the fusion calibration algorithm.

摘要

基于微机电系统（MEMS）阵列的惯性导航模块（M-IMU）通过融合多种数据处理方式来减少MEMS传感器的测量奇异性，以提高其导航性能。然而，M-IMU导航中仍然存在随机误差和固定误差。该校准方法对M-IMU的固定误差参数进行校准，以进一步提高导航精度。在本文中，我们提出了一种低成本且高效的校准方法，以有效估计M-IMU的固定误差参数。首先，我们手动将M-IMU旋转到多组不同姿态（静止状态），然后使用LM校准算法在静止-动态滤波器分离的不同区间内优化相应传感器的代价函数，以获得MEMS的固定误差参数，最后，基于基准转换，通过对各个MEMS固定误差参数进行自适应支持融合来校准M-IMU的全局固定误差参数。通过融合校准算法和LM校准算法分别对MEMS进行校准的比较验证了，在多维测试环境中，与单个MEMS相比，校准后的MEMS阵列测量精度提高了约10分贝，输出数据的离散度降低了约8分贝，表明了融合校准算法的鲁棒性和可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/fca365689d6b/micromachines-13-01214-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/48524a017b2c/micromachines-13-01214-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/d08220acbab2/micromachines-13-01214-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/ef88b317267a/micromachines-13-01214-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/fca365689d6b/micromachines-13-01214-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/373d2e9f4dfd/micromachines-13-01214-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/f51eadd608c7/micromachines-13-01214-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/3a6cee4f420a/micromachines-13-01214-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/05db0a436633/micromachines-13-01214-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/c90db209647b/micromachines-13-01214-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/48524a017b2c/micromachines-13-01214-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/9a72419904d8/micromachines-13-01214-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/d08220acbab2/micromachines-13-01214-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/ef88b317267a/micromachines-13-01214-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f8/9415369/fca365689d6b/micromachines-13-01214-g012.jpg

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本文引用的文献

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Portable Gait Lab: Estimating 3D GRF Using a Pelvis IMU in a Foot IMU Defined Frame.便携式步态实验室：在由足部惯性测量单元定义的坐标系中使用骨盆惯性测量单元估计三维地面反作用力
IEEE Trans Neural Syst Rehabil Eng. 2020 Jun;28(6):1308-1316. doi: 10.1109/TNSRE.2020.2984809. Epub 2020 Apr 20.