Gyro Modeling and Estimation of Its Random Noise Sources

Lam, Quang M.; Stamatakos, Nick; Woodruff, C.; Ashton, Sandy; Aerospace, Swales

doi:10.2514/6.2003-5562

Cited by 39 publications

(32 citation statements)

References 2 publications

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“…Most of currently employed signal processing methodologies utilize Kalman filtering techniques to reduce the effect of ARW and to improve the estimation accuracy [15, 16]. Unfortunately, the convergence of these methods (the time to remove the effect of the ARW and to provide an accurate estimate) during the alignment processes can take up to 15 min.…”

Section: Minimal-drift Heading Angle Measurementmentioning

confidence: 99%

Minimal-Drift Heading Measurement using a MEMS Gyro for Indoor Mobile Robots

Hong

Park

2008

Sensors

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To meet the challenges of making low-cost MEMS yaw rate gyros for the precise self-localization of indoor mobile robots, this paper examines a practical and effective method of minimizing drift on the heading angle that relies solely on integration of rate signals from a gyro. The main idea of the proposed approach is consists of two parts; 1) self-identification of calibration coefficients that affects long-term performance, and 2) threshold filter to reject the broadband noise component that affects short-term performance. Experimental results with the proposed phased method applied to Epson XV3500 gyro demonstrate that it effectively yields minimal drift heading angle measurements getting over major error sources in the MEMS gyro output.

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Section: Minimal-drift Heading Angle Measurementmentioning

confidence: 99%

Minimal-Drift Heading Measurement using a MEMS Gyro for Indoor Mobile Robots

Hong

Park

2008

Sensors

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“…Because the filter precision is primarily determined by the process noise covariance matrix, it is determined by the IFOG stochastic noise sources. However, the IFOG is mainly composed of fiber coil that is relatively unstable in a temperature varied environment, and the performance degrades with aging and unexpected failure which requires detection and correction either on the ground or onboard, and then the corrections are uploaded to the gyrocompass to preserve life [4]. In some sense, a good knowledge of IFOG stochastic noise sources is the representative of IFOG's "state of health".…”

Section: Introductionmentioning

confidence: 99%

A novel IFOG Allan variance coefficients real-time detection method with temperature variation in moorage

Gao

Guan

Wang

et al. 2015

Optik - International Journal for Light and Electron Optics

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“…The conventional ARMA modeling methods for gyro random noise, such as least square method, moment estimation method, and maximum likelihood method, all must first determine the model order before the ARMA model parameters are estimated. The order determination processes are complex, and the parameter estimations of the conventional methods require a large sample size and have a slow convergence speed [4,5,6,7]. Hence, they cannot be applied to applications in which a fast and accurate ARMA modeling method for gyro random noise is required.…”

Section: Introductionmentioning

confidence: 99%

Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

Huang

2015

Sensors

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To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.

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Gyro Modeling and Estimation of Its Random Noise Sources

Cited by 39 publications

References 2 publications

Minimal-Drift Heading Measurement using a MEMS Gyro for Indoor Mobile Robots

Minimal-Drift Heading Measurement using a MEMS Gyro for Indoor Mobile Robots

A novel IFOG Allan variance coefficients real-time detection method with temperature variation in moorage

Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

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