Denoising Method of MEMS Gyroscope Based on Interval Empirical Mode Decomposition

The microelectromechanical system (MEMS) gyroscope has low measurement accuracy and large output noise; the useful signal is often submerged in the noise. A new denoising method of interval empirical mode decomposition (IEMD) is proposed. Firstly, the traditional EMD algorithm is used to decompose the signal into a finite number of intrinsic mode functions (IMFs). Based on the Bhattacharyya distance analysis and the characteristics of the autocorrelation function, a screening mechanism is proposed to divide IMFs into three categories: noise IMFs, mixed IMFs, and signal IMFs. Then, the traditional modelling filtering method is used to filter the mixed IMFs. Finally, the mixed IMFs after modelling and filtering and signal IMFs are reconstructed to obtain the denoised signal. In the experimental analysis, the static denoising experiment of the turntable, the Allan variance analysis, dynamic denoising experiment, and vehicle experiment are set up in this paper, which fully proves that the method has obvious advantages in denoising and greatly improves the quality of signal and the accuracy of the inertial navigation system solution.

[2]  Xiu Yun Meng,et al.  Research on Time-series Modeling and Filtering Methods for MEMS Gyroscope Random Drift Error , 2017 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Jun Liu,et al.  High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold , 2019, Micromachines.

[5]  Ning Liu,et al.  Function Extension Based Real-Time Wavelet De-Noising Method for Projectile Attitude Measurement , 2019, Sensors.

[6]  Ching-Hsin Wang,et al.  Gyro motor fault classification model based on a coupled hidden Markov model with a minimum intra-class distance algorithm , 2020, J. Syst. Control. Eng..

[7]  Albert C. S. Chung,et al.  A novel learning-based dissimilarity metric for rigid and non-rigid medical image registration by using Bhattacharyya Distances , 2017, Pattern Recognit..

[8]  陈光武 Chen Guang-wu,et al.  MEMS gyro denoising based on fuzzy interval threshold EMD , 2019 .

[9]  Liu Feng,et al.  Denoising algorithm of Φ_OTDR signal based on clear iterative EMD interval-thresholding , 2019 .

[10]  Zhelong Wang,et al.  A time-controllable Allan variance method for MEMS IMU , 2013, Ind. Robot.

[11]  Jagannath Nayak,et al.  ARMA model based adaptive unscented fading Kalman filter for reducing drift of fiber optic gyroscope , 2016 .

[12]  Yuwei Chen,et al.  Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising , 2018, Sensors.

[13]  Yuanyuan Liu,et al.  EMD interval thresholding denoising based on similarity measure to select relevant modes , 2015, Signal Process..

[14]  Wu Deng,et al.  Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem , 2020, Appl. Soft Comput..

[15]  Xueye Wei,et al.  De-noising of Magnetic Flux Leakage Signals Based on Wavelet Filtering Method , 2018, Research in Nondestructive Evaluation.

[16]  Jianye Liu,et al.  Allan variance method for gyro noise analysis using weighted least square algorithm , 2015 .

[17]  Huiliang Cao,et al.  A Temperature Error Parallel Processing Model for MEMS Gyroscope based on a Novel Fusion Algorithm , 2020, Electronics.