A Signal Processing Technique for Improving the Accuracy of MEMS Inertial Sensors

Stubberud, Peter; Stubberud, Allen R.

doi:10.1109/icseng.2008.25

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…The fusion method used in the works of Bayard and Pleon was the Kalman filter. Then, an extended Kalman filter to fuse multiple gyroscopes for accuracy improvement was presented by Stubberud [12]. After that, Chang, Xue, and Jiang at Northwestern Polytechnical University played an important role in promoting the development of gyro arrays.…”

Section: Introductionmentioning

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: Introductionmentioning

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 “virtual gyroscope” was first proposed by Bayard and Ploen (2003), which combined four gyroscope readings and obtained an optimal estimated rate signal by the Kalman filter. A two-level optimal filtering method (Chang et al , 2008) and the extended Kalman filter to fuse multiple gyroscopes for accuracy improvement (Stubberud and Stubberud, 2008) were presented. Jiang and Xue constructed an array with six gyroscopes (Xue et al , 2012; Chang et al , 2012; Jiang et al , 2012; Xue et al , 2015) and adopted a novel optimal Kalman filter to process their signal.…”

Section: Introductionmentioning

confidence: 99%

Kurtosis-based IMM filter for multiple MEMS gyroscopes fusion

Shen

Liu

Huang

et al. 2017

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Purpose The purpose of this study is to explore a signal processing method to improve the angular rate accuracy of micro-electro-mechanical system (MEMS) gyroscope by combining numerous gyroscopes. Design/methodology/approach To improve the dynamic performance of the signal processing method, the interacting multiple model (IMM) can be applied to the fusion of gyroscope array. However, the standard IMM has constant Markov parameter, which may reduce the model switching speed. To overcome this problem, an adaptive IMM filter is developed based on the kurtosis of the gyroscope output, in which the transition probabilities are adjusted online by utilizing the dynamic information of the rate signal. Findings The experimental results indicate that the precision of the gyroscope array composed of six gyroscopes increases significantly and the kurtosis-based adaptive Markov parameter IMM filter (K-IMM) performs better than the baseline methods, especially under dynamic conditions. These experiments prove the validity of the proposed fusion method. Practical implications The proposed method can improve the accuracy of MEMS gyroscopes without breakthrough on hardware, which is necessary to extend their utility while not restricting the overwhelming advantages. Original/value A K-IMM algorithm is proposed in this paper, which is used to improve the angular rate accuracy of MEMS gyroscope by combining numerous gyroscopes.

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“…Al-Majed and Alsuwaidan presented a multi-filters adaptive estimator for the MEMS gyroscope array system to improve the angular rate estimates [ 11 ]. Additionally, an extended Kalman filter (EKF) technique for combining multiple sensors was studied in [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Signal Processing of MEMS Gyroscope Arrays to Improve Accuracy Using a 1st Order Markov for Rate Signal Modeling

Jiang

Xue

Chang

et al. 2012

Sensors

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This paper presents a signal processing technique to improve angular rate accuracy of the gyroscope by combining the outputs of an array of MEMS gyroscope. A mathematical model for the accuracy improvement was described and a Kalman filter (KF) was designed to obtain optimal rate estimates. Especially, the rate signal was modeled by a first-order Markov process instead of a random walk to improve overall performance. The accuracy of the combined rate signal and affecting factors were analyzed using a steady-state covariance. A system comprising a six-gyroscope array was developed to test the presented KF. Experimental tests proved that the presented model was effective at improving the gyroscope accuracy. The experimental results indicated that six identical gyroscopes with an ARW noise of 6.2 °/√h and a bias drift of 54.14 °/h could be combined into a rate signal with an ARW noise of 1.8 °/√h and a bias drift of 16.3 °/h, while the estimated rate signal by the random walk model has an ARW noise of 2.4 °/√h and a bias drift of 20.6 °/h. It revealed that both models could improve the angular rate accuracy and have a similar performance in static condition. In dynamic condition, the test results showed that the first-order Markov process model could reduce the dynamic errors 20% more than the random walk model.

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A Signal Processing Technique for Improving the Accuracy of MEMS Inertial Sensors

Cited by 11 publications

References 3 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

Kurtosis-based IMM filter for multiple MEMS gyroscopes fusion

Signal Processing of MEMS Gyroscope Arrays to Improve Accuracy Using a 1st Order Markov for Rate Signal Modeling

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