Statistical Modeling of Rate Gyros

Vaccaro, R.J.; Zaki, Ahmed S.

doi:10.1109/tim.2011.2171609

Cited by 73 publications

(58 citation statements)

References 5 publications

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“…It must be noted that this method could also be based on the AV since the Haar WV is simply twice the AV. Having said this, in a way this estimator can be viewed as a generalized version of [8] with additional benefits. Among these benefits we can highlight its good statistical properties, its ease of implementation and its computational efficiency.…”

Section: Methods Of Wavelet Moments (Gmwm) Based On the Idea Of Genermentioning

confidence: 99%

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A Computationally Efficient Framework for Automatic Inertial Sensor Calibration

et al. 2018

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The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3D-animation.However, this calibration procedure is often quite problematic since the signals issued from these sensors have a complex spectral structure and the methods available to estimate the parameters of these models are either unstable, computationally intensive and/or statistically inconsistent. This paper presents a new software platform for inertial sensor calibration based on the Generalized Method of Wavelet Moments which provides a computationally efficient, flexible, user-friendly and statistically sound tool to estimate and select from a wide range of complex models. In addition, all this is possible also in a robust framework allowing to perform sensor calibration when the data is affected by outliers. The software is developed within the open-source statistical software R and is based on C++ language allowing it to achieve high computational performance.

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Section: Methods Of Wavelet Moments (Gmwm) Based On the Idea Of Genermentioning

confidence: 99%

“…Nevertheless, although still being widely used, [7] formally proved that this method is inefficient and inconsistent in the vast majority of cases. Considering this, a different use of the AV was presented in [8] where the parameters of a process composed by a white noise and a random walk were simultaneously estimated in a method of moments fashion.…”

mentioning

confidence: 99%

A Computationally Efficient Framework for Automatic Inertial Sensor Calibration

et al. 2018

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“…From the definition of correlation factor, and the use of Equations (13), (15) and (16), it can be obtained:

ρ_{y_{1}, y_{2}} = \frac{ρ_{b_{1}, b_{2}}}{\sqrt{false(σ_{d_{1}}^{2} + σ_{n_{1}}^{2} false) false(σ_{d_{2}}^{2} + σ_{n_{2}}^{2} false)} / σ_{d_{1}} σ_{d_{2}}}

where ρ b 1, b 2 is the correlation factor between the bias drift b 1 and b 2 in the gyroscope array, and ρ y 1, y 2 is the correlation factor between the gyroscope outputs of y 1 and y 2 . The variances of

σ_{n}^{2}

and

σ_{d}^{2}

can be obtained by the Allan variance technique [23]. Therefore, under the condition of static test, by the use of Equation (17), the correlation factor ρ b 1, b 2 can be obtained from the ρ y 1, y 2 directly computed from the outputs of the gyroscope units.…”

Section: Analysis Of Correlation Factor In Mems Gyroscope Arraymentioning

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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“…The Allan variance method is usually used to identify and quantify the stochastic noise statistics of a MEMS gyroscope [19,20]. The definition of Allan variance for the typical gyroscope model of Equation (1) …”

Section: Modeling Of Stochastic Error For Mems Gyroscopementioning

confidence: 99%

Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

et al. 2015

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Abstract:In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor's information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h 0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h 0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. OPEN ACCESSMicromachines 2015, 6 267

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Statistical Modeling of Rate Gyros

Cited by 73 publications

References 5 publications

A Computationally Efficient Framework for Automatic Inertial Sensor Calibration

A Computationally Efficient Framework for Automatic Inertial Sensor Calibration

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

Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

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