Analysis of Dynamic Performance of a Kalman Filter for Combining Multiple MEMS Gyroscopes

Xue, Liang; Wang, Lixin; Xiong, Tao; Jiang, Chengyu; Yuan, Weizheng

doi:10.3390/mi5041034

Cited by 20 publications

(19 citation statements)

References 19 publications

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“…This problem requires determination of ways to design the multi-resonator sensor or array of sensors with redundant output information about angle rate of an object along one axis [2]. Integration of redundant information about angle rate, for example, by means of Kalman filter [3,4], and extended Kalman filter [5] in complicated cases leads to the necessity of mathematical modelling of the measured angle rate. For example, the angle rate model in [6] is represented by a Markov process of the first order, and the general auto-regression model conditionally heteroscedastic model of the angle rate is used in [7].…”

Section: Analysis Of Publications Data and Problem Statementmentioning

confidence: 99%

Redundant information processing techniques comparison for differential vibratory gyroscope

Chikovani

Sushchenko

Tsiruk

2016

EEJET

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Section: Analysis Of Publications Data and Problem Statementmentioning

confidence: 99%

Redundant information processing techniques comparison for differential vibratory gyroscope

Chikovani

Sushchenko

Tsiruk

2016

EEJET

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“…Al-Majed and Alsuwaidan presented a multi-filters adaptive estimator to improve the angular rate accuracy by using the noise correlation in [7] but with no simulation or experimental results. Jiang and Xue used six-gyro array in [8][9][10][11]. They also adopted a novel optimal Kalman filter to combine the gyroscopes [8].…”

Section: Open Accessmentioning

confidence: 99%

Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation

Liu¹,

Shen²,

Qin³

2015

Micromachines

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Abstract:A signal processing technique is presented to improve the angular rate accuracy of Micro-Electro-Mechanical System (MEMS) gyroscope by combining numerous gyroscopes. Based on the conditional correlation between gyroscopes, a dynamic data fusion model is established. Firstly, the gyroscope error model is built through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process to improve overall performance. Then the conditional covariance obtained through dynamic conditional correlation (DCC) estimator is used to describe the correlation quantitatively. Finally, the approach is validated by a prototype of the virtual gyroscope, which consists of six-gyroscope array. The experimental results indicate that the weights of gyroscopes change with the value of error. Also, the accuracy of combined rate signal is improved dramatically compared to individual gyroscope. The results indicate that the approach not only improves the accuracy of the MEMS gyroscope, but also discovers the fault gyroscope and eliminates its influence.

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“…The technology of multiple-sensor fusion provides a new way for improving the precision of a MIMU [9,10,11,12,13,14]. An array of MEMS gyroscopes can be configured and mounted on each orthogonally sensitive axis of a MIMU to provide redundant signals at the same condition.…”

Section: Introductionmentioning

confidence: 99%

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

Xue

Wang

et al. 2018

Micromachines

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Obtaining a correlation factor is a prerequisite for fusing multiple outputs of a mircoelectromechanical system (MEMS) gyroscope array and evaluating accuracy improvement. In this paper, a mathematical statistics method is established to analyze and obtain the practical correlation factor of a MEMS gyroscope array, which solves the problem of determining the Kalman filter (KF) covariance matrix Q and fusing the multiple gyroscope signals. The working principle and mathematical model of the sensor array fusion is briefly described, and then an optimal estimate of input rate signal is achieved by using of a steady-state KF gain in an off-line estimation approach. Both theoretical analysis and simulation show that the negative correlation factor has a favorable influence on accuracy improvement. Additionally, a four-gyro array system composed of four discrete individual gyroscopes was developed to test the correlation factor and its influence on KF accuracy improvement. The result showed that correlation factors have both positive and negative values; in particular, there exist differences for correlation factor between the different units in the array. The test results also indicated that the Angular Random Walk (ARW) of 1.57°/h0.5 and bias drift of 224.2°/h for a single gyroscope were reduced to 0.33°/h0.5 and 47.8°/h with some negative correlation factors existing in the gyroscope array, making a noise reduction factor of about 4.7, which is higher than that of a uncorrelated four-gyro array. The overall accuracy of the combined angular rate signal can be further improved if the negative correlation factors in the gyroscope array become larger.

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Analysis of Dynamic Performance of a Kalman Filter for Combining Multiple MEMS Gyroscopes

Cited by 20 publications

References 19 publications

Redundant information processing techniques comparison for differential vibratory gyroscope

Redundant information processing techniques comparison for differential vibratory gyroscope

Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation

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

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