Analysis of Correlation in MEMS Gyroscope Array and its Influence on Accuracy Improvement for the Combined Angular Rate Signal

Xue, Liang; Wang, Xinguo; Bo, Yang; Yuan, Weizheng; Yuan, Guangmin

doi:10.3390/mi9010022

Cited by 17 publications

(11 citation statements)

References 23 publications

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“…Through multiple tests, the correlation factor between the component gyroscopes was obtained and is shown in Table 1: It played an important role in accuracy improvement of the gyro array [28]. This meant the elements in boldnormalCorrM were given.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Multi-Model Combined Filter with Dual Uncertainties for Data Fusion of MEMS Gyro Array

Shen

Liu

Zhou

et al. 2018

Sensors

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The gyro array is a useful technique in improving the accuracy of a micro-electro-mechanical system (MEMS) gyroscope, but the traditional estimate algorithm that plays an important role in this technique has two problems restricting its performance: The limitation of the stochastic assumption and the influence of the dynamic condition. To resolve these problems, a multi-model combined filter with dual uncertainties is proposed to integrate the outputs from numerous gyroscopes. First, to avoid the limitations of the stochastic and set-membership approaches and to better utilize the potentials of both concepts, a dual-noise acceleration model was proposed to describe the angular rate. On this basis, a dual uncertainties model of gyro array was established. Then the multiple model theory was used to improve dynamic performance, and a multi-model combined filter with dual uncertainties was designed. This algorithm could simultaneously deal with stochastic uncertainties and set-membership uncertainties by calculating the Minkowski sum of multiple ellipsoidal sets. The experimental results proved the effectiveness of the proposed filter in improving gyroscope accuracy and adaptability to different kinds of uncertainties and different dynamic characteristics. Most of all, the method gave the boundary surrounding the true value, which is of great significance in attitude control and guidance applications.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Multi-Model Combined Filter with Dual Uncertainties for Data Fusion of MEMS Gyro Array

Shen

Liu

Zhou

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…The random error model in reference [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ] only considers two random drift errors of angular random walk and angular rate random walk, neglecting the multiple random errors, and the virtual gyroscope technology can’t achieve the compensation of all random error. Therefore, the improved random error model of the MEMS gyroscope is established by combining Equations (4) and (19).…”

Section: Virtual Gyroscope Technology Based On Arma Modelmentioning

confidence: 99%

“…There are four MEMS gyroscopes integrated on one chip, which improves the correlation between the gyroscopes [ 12 ]. The relationship between correlation coefficient and data fusion results of MEMS gyroscope array is fully analyzed [ 13 ]. The design of the MEMS gyro array with 3 × 3 plane structure is carried out [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Virtual Gyroscope Technology Based on the ARMA Model

et al. 2018

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In view of the large output noise and low precision of the Micro-electro-mechanical Systems (MEMS) gyroscope, the virtual gyroscope technology was used to fuse the data of the MEMS gyroscope to improve its output precision. Random error model in the conventional virtual gyroscopes contained an angular rate random walk and angle random walk ignoring other noise items and the virtual gyroscope technology can not compensate all random errors of MEMS gyroscope. So, the improved virtual gyroscope technology based on the autoregressive moving average (ARMA) model was proposed. First, the conventional virtual gyroscope technology was used to model the random error of three MEMS gyroscopes, and the data fusion was carried out by a Kalman filter to get the output of the virtual gyroscope. After that, the ARMA model was used to model the output of the virtual gyroscope, the random error model was improved with the ARMA model, and the Kalman filter was designed based on the improved random error model for data fusion of the MEMS gyroscopes. The experimental results showed that the 1σ standard deviation of the output of the virtual gyroscope based on the ARMA model was 1.4 times lower than that of the conventional virtual gyroscope output.

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“…The random noises include angular random walk (ARW), rate random walk (RRW), bias instability (1/f noise), quantisation noise etc., and their influence could be reduced by modelling them and filtering [5]. However, almost of inertial sensors have ARW and RRW as their dominant random noise components [1–3, 7]. Thus, characterizing these two random noises more accurately is a prerequisite for developing a state space model of the inertial sensors [7].…”

Section: Introductionmentioning

confidence: 99%

Statistical modelling of rate gyros based on fully overlapping Allan variance

Ri¹,

Sin²,

Kang³

2021

IET Science Measure & Tech

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Angular random walk and rate random walk are two dominant random noise components which are inherent in almost gyroscopes. Therefore, modelling these noise components accurately is very important. Here, a modified algorithm is proposed for estimating the two random noise components accurately. Up to now, the statistical modelling of the two random noise components has been conducted by the best linear unbiased estimator, which is based on the formulae for mean, variance and covariance of non‐overlapping Allan variance of them, that is, angular random walk and rate random walk. The modified algorithm is developed by using the best linear unbiased estimator, based on formulae for mean, variance and covariance of fully‐overlapping Allan variance which are newly derived in this paper. Efficiency of the algorithm is evaluated by simulation and experiment.

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Analysis of Correlation in MEMS Gyroscope Array and its Influence on Accuracy Improvement for the Combined Angular Rate Signal

Cited by 17 publications

References 23 publications

A Multi-Model Combined Filter with Dual Uncertainties for Data Fusion of MEMS Gyro Array

A Multi-Model Combined Filter with Dual Uncertainties for Data Fusion of MEMS Gyro Array

Improved Virtual Gyroscope Technology Based on the ARMA Model

Statistical modelling of rate gyros based on fully overlapping Allan variance

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