A linear fusion algorithm for attitude determination using low cost MEMS-based sensors

Zhu, Rong; Sun, Dong; Zhou, Zucheng; Wang, Dingqu

doi:10.1016/j.measurement.2006.05.020

Cited by 113 publications

(59 citation statements)

References 5 publications

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“…The characteristics of this algorithm fit very well into the solution of a number of problems arising in the process of accuracy optimization of the measuring instruments that define the parameters of all moving objects listed above [10]. In that case the algorithm will be developed in the context of the set objective, which is related to the improvement of the dynamic accuracy of the measuring system presented in [9].…”

Section: Structural Model Of the Algorithmmentioning

confidence: 95%

A Kalman Filter-Based Algorithm for Measuring the Parameters of Moving Objects

Dichev¹,

Koev²,

Bakalová³

et al. 2015

Measurement Science Review

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One of the most complex problems in measuring equipment is related to the provision of the required dynamic accuracy of measuring systems determining the parameters of moving objects. The present paper views an algorithm for improving the dynamic accuracy of such measuring systems. It is based on the Kalman method. The algorithm aims to eliminate the influence of a number of interference sources, each of which is of secondary significance. However, their total effect can cause considerable distortion of the measurement signal. The algorithm model is designed for gyro-free measuring systems. It is based on one of the most widely used elements in the dynamic systems, namely the physical pendulum, due to which measuring systems of high dynamic accuracy and low cost can be developed. The presented experimental results confirm the effectiveness of the proposed algorithm with respect to the dynamic accuracy of measuring systems of this type.

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Section: Structural Model Of the Algorithmmentioning

confidence: 95%

A Kalman Filter-Based Algorithm for Measuring the Parameters of Moving Objects

Dichev¹,

Koev²,

Bakalová³

et al. 2015

Measurement Science Review

View full text Add to dashboard Cite

show abstract

“…Most of these consist of various Kalman filter solutions [2,3,5,12,13,16,25], usually extended Kalman filters (EKF) [7,9,10,15,17,18,20,26], and some unscented Kalman filters (UKF) [14,19,27], though some non-Kalman filter solutions also exist [1,4,11,21,[28][29][30] as well as some geometric methods [31][32][33][34]. In addition, Chao et al [35] have carried out a comparative study of low-cost IMU filters.…”

Section: Related Workmentioning

confidence: 99%

A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

Hyyti

Visala

2015

International Journal of Navigation and Observation

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An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanicalsystem (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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“…The latter have their own specific features depending on the type and characteristics of the moving objects, measured quantities and interference effects, as well as on the dynamic properties of measuring instruments. With regard to the above mentioned there are various types of adaptive algorithms which differ in their computational complexity, behavior patterns, used output data, structure of the adapting systems themselves [6,7]. Integration of such algorithms in the metrological chain of systems measuring parameters of moving objects is a good perspective for the enhancement of measuring equipment in this area since it considerably increases the measurement accuracy in the dynamic mode without using expensive elements and units in the measuring system structure [8].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Improving the Accuracy of Systems Measuring Parameters of Moving Objects

Dichev¹,

Koev²,

Bakalová³

et al. 2016

Metrology and Measurement Systems

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The paper considers an algorithm for increasing the accuracy of measuring systems operating on moving objects. The algorithm is based on the Kalman filter. It aims to provide a high measurement accuracy for the whole range of change of the measured quantity and the interference effects, as well as to eliminate the influence of a number of interference sources, each of which is of secondary importance but their total impact can cause a considerable distortion of the measuring signal. The algorithm is intended for gyro-free measuring systems. It is based on a model of the moving object dynamics. The mathematical model is developed in such a way that it enables to automatically adjust the algorithm parameters depending on the current state of measurement conditions. This makes possible to develop low-cost measuring systems with a high dynamic accuracy. The presented experimental results prove effectiveness of the proposed algorithm in terms of the dynamic accuracy of measuring systems of that type.

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A linear fusion algorithm for attitude determination using low cost MEMS-based sensors

Cited by 113 publications

References 5 publications

A Kalman Filter-Based Algorithm for Measuring the Parameters of Moving Objects

A Kalman Filter-Based Algorithm for Measuring the Parameters of Moving Objects

A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

An Algorithm for Improving the Accuracy of Systems Measuring Parameters of Moving Objects

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