Unmanned Noise Reduction Method of Micro-Electro-Mechanical System Inertial Measurement Unit Based on Improved EMD

Zhenpeng Zhang, Qian Sun, Yuan Tian
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Abstract

The traditional Fourier algorithm cannot fix the problem of non-stationary noise deduction for MEMS-IMU, therefore this article uses Empirical Mode Decomposition (EMD) algorithm to denoise the signal. In this article, Extreme Learning Machine (ELM) is combined to reduce the influence of end effect in the decomposition. First, the MEMS-IMU simulate signal generated by matlab is taken as the test object, and EMD as well as ELM extension decomposition are carried out for it respectively. The decomposition noise reduction effect is compared and analyzed to study the role of EMD and ELM in the process. Second, carry out a test on the authentically measured MEMS-IMU signal. In de-noising the MEMS-IMU simulate signal and authentic signal, we can analyze the noise deduction effect and observe the changes of parameters related to random error. The results show that the method based on ELM and EMD can achieve good noise reduction effect for MEMS signal.
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基于改进EMD的微机电系统惯性测量单元无人降噪方法
传统的傅立叶算法无法解决MEMS-IMU的非平稳降噪问题,因此本文采用经验模态分解(EMD)算法对信号进行降噪。本文结合极限学习机(Extreme Learning Machine, ELM)来降低分解过程中末端效应的影响。首先,以matlab生成的MEMS-IMU仿真信号为测试对象,分别对其进行EMD和ELM扩展分解。对比分析了分解降噪效果,研究了EMD和ELM在分解降噪过程中的作用。其次,对真实测量的MEMS-IMU信号进行测试。在对MEMS-IMU仿真信号和真实信号进行降噪时,可以分析降噪效果,观察随机误差相关参数的变化。结果表明,基于ELM和EMD的方法对MEMS信号具有较好的降噪效果。
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