Modified Adaptive Real-time Filtering Algorithm for MEMS Gyroscope Random Noise

Fu, Jun; Han, Hongxiang

doi:10.3788/gzxb20194812.1212003

Cited by 4 publications

(4 citation statements)

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“…The system noise is mainly low-frequency noise, while the measurement noise is wideband noise. By using the Allan variance filter, low-frequency noise can be directly filtered out, so the Allan variance of the wideband noise can be approximated as the variance of the measurement noise [26]. The Allan variance with the correlation time as the minimum sampling interval (τ 0 ) can be rewritten in recursive form as…”

Section: Improved Sage-husa Adaptive Filtering Based On Armamentioning

confidence: 99%

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A method for compensating random errors in MEMS gyroscopes based on interval empirical mode decomposition and ARMA

Zeng,

Xian,

Liu

et al. 2023

Meas. Sci. Technol.

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The random error in micro-electro-mechanical systems (MEMS) gyroscopes is one of the major aspects that limit measurement accuracy. In order to address the inaccurate extraction of noise and trend during the signal preprocessing, as well as the subjectivity in autoregressive moving average (ARMA) model ordering, this paper proposes a method based on interval empirical mode decomposition and ARMA model. In the proposed method, the original signal is decomposed into a series of intrinsic mode functions (IMFs) and a residual through empirical mode decomposition (EMD). Based on the Hellinger distance and autocorrelation function, IMFs are then classified into noise IMFs, hybrid IMFs, and signal IMFs. The improved sand cat swarm optimization is utilized to optimize the ordering process of the ARMA model. The improved adaptive filter is adopted to compensate the random error, and the compensated signal is reconstructed with the signal IMFs and residual to obtain the final output. Experiments show that under static conditions, the proposed method could reduce the root mean square error (RMSE) by 52.6% and 33.3%, respectively, compared with the traditional EMD and ARMA methods. Under dynamic conditions, the proposed method could reduce the RMSE by 51.1% and 37.1%, respectively, compared with the traditional EMD and ARMA methods. The proposed method could effectively compensate the random error and improve the measurement accuracy of MEMS gyroscopes.

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Section: Improved Sage-husa Adaptive Filtering Based On Armamentioning

confidence: 99%

“…where β i is the weight sequence of measurement noise, and b follows the definition in equation (26).…”

Section: Improved Sage-husa Adaptive Filtering Based On Armamentioning

confidence: 99%

A method for compensating random errors in MEMS gyroscopes based on interval empirical mode decomposition and ARMA

Zeng,

Xian,

Liu

et al. 2023

Meas. Sci. Technol.

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“…engineering significance to accurately model and effectively suppress the stochastic error term of IFOG. In this regard, scholars have carried out a lot of research work, and IFOG stochastic error suppression methods based on low-pass filtering (LPF), wavelet transform (WT), empirical mode decomposition (EMD), Kalman filtering (KF), etc, have been proposed one after another [6][7][8][9][10]. However, LPF, WT, EMD and other methods are not applicable to low-frequency noise analysis (long-time error) [11], while the WT and EMD methods are too complex and difficult to implement for the limited computational resources of SINS, and the real-time performance of the algorithms is not good [12].…”

Section: Introductionmentioning

confidence: 99%

Research on stochastic error suppression method of FOG based on quasi-proportional information

Liang,

Zhou,

et al. 2024

Meas. Sci. Technol.

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In order to improve the precision of FOG, it is necessary to suppress the stochastic error effectively. Therefore, the stochastic error suppression of FOG is studied. Firstly, the data of FOG at room temperature and variable temperature are analyzed. Then, a general form of FOG random error model is derived, and an adaptive filtering method based on quasi-proportional information is proposed to estimate the process noise covariance effectively. At last, the room temperature experiment, the variable temperature experiment and the dynamic experiment are carried out. Compared with the recently proposed adaptive filter algorithm, the variance reduction of the proposed algorithm at the average time of 33s, 54s and 55s is more than 70%, and the suppression effect of the algorithm on the random walk coefficient is improved by more than 2%, showing strong robustness.

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“…The dual-antenna combined with the MEMS gyroscope's steering angle measurement scheme reduces the system cost, but due to the low update rate of the dual-antenna GNSS heading, the measurement accuracy is not high [22,23] , which affects the accuracy of the measurement system, and the accelerometer's installation position is difficult to match with the swing reference point of the carrier. In an angular motion environment, the output of the accelerometer produces interference acceleration relative to the reference point, which is affected by the lever arm effect [24][25][26][27][28] , which also affects the measurement accuracy.…”

Section: Introduction mentioning

confidence: 99%

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

Wang¹,

Hu²,

He³

et al. 2022

International Journal of Agricultural and Biological Engineering

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Aiming at the application environment of paddy agricultural machinery with bumpy and undulating changes, the problems affecting the method for steering wheel angle measurement by MEMS gyroscope were analyzed, and a wheel angle measurement method combining Dual-MEMS gyroscope (dual MEMS gyroscope) and RTK-GNSS was designed. The adaptive weighting method was used to fuse the heading angle differentiation of RTK-GNSS, the MEMS gyroscope angle rate, and velocity data, and the rod-arm compensation was performed to accurately obtain the angle rates of the body and steering wheels of agricultural machinery; the difference between the combined angular rate of the steering wheel of the agricultural machinery and the angular rate of the agricultural machinery body was obtained, and the integrator is used to integrate the difference to get the wheel steering angle value, and the Kalman filter was designed to make feedback correction for the integration process of angle calculation to eliminate the errors caused by the gyroscope zero bias, random drift, and gyroscope rod arm effect, and to obtain the accurate value of wheel steering angle. A comparative test with the connecting rod wheel angle sensor was designed, and the results show that the maximum deviation is 4.99°, the average absolute average value is 1.61°, and the average standard deviation is 0.98°. The method in this study and the connecting rod wheel angle sensor were used on paddy farm machinery. The wheel angle measurement deviation of the proposed method and the connecting rod wheel angle sensor was not more than 1°, which is relatively small. It has good stability, speed adaptability, and dynamic responsiveness that meets the accuracy requirements of steering wheel angle measurement for paddy field agricultural machinery unmanned driving and can be used instead of connecting rod angle sensors for unmanned agricultural machinery.

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Modified Adaptive Real-time Filtering Algorithm for MEMS Gyroscope Random Noise

Cited by 4 publications

References 0 publications

A method for compensating random errors in MEMS gyroscopes based on interval empirical mode decomposition and ARMA

A method for compensating random errors in MEMS gyroscopes based on interval empirical mode decomposition and ARMA

Research on stochastic error suppression method of FOG based on quasi-proportional information

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

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