Statistical modelling of rate gyros based on fully overlapping Allan variance

Ri, Yong Gil; Sin, Chol Myong; Kang, Jun Gol

doi:10.1049/smt2.12080

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Therefore, parameters are usually estimated by performing linear regression of (visually) identified linear regions in such log–log plots. Further research on this principle can be found in [ 8 , 9 , 10 , 25 , 26 ]. A GMWM-based algorithm can be considered as a further extension of AV because they both identify and quantify the different noise terms that exist in inertial sensor data.…”

Section: Methodsmentioning

confidence: 99%

An Algorithm for Online Stochastic Error Modeling of Inertial Sensors in Urban Cities

Zhao

2023

Sensors

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Regardless of whether the global navigation satellite system (GNSS)/inertial navigation system (INS) is integrated or the INS operates independently during GNSS outages, the stochastic error of the inertial sensor has an important impact on the navigation performance. The structure of stochastic error in low-cost inertial sensors is quite complex; therefore, it is difficult to identify and separate errors in the spectral domain using classical stochastic error methods such as the Allan variance (AV) method and power spectral density (PSD) analysis. However, a recently proposed estimation, based on generalized wavelet moment estimation (GMWM), is applied to the stochastic error modeling of inertial sensors, giving significant advantages. Focusing on the online implementation of GMWM and its integration within a general navigation filter, this paper proposes an algorithm for online stochastic error calibration of inertial sensors in urban cities. We further develop the autonomous stochastic error model by constructing a complete stochastic error model and determining model ranking criterion. Then, a detecting module is designed to work together with the autonomous stochastic error model as feedback for the INS/GNSS integration. Finally, two experiments are conducted to compare the positioning performance of this algorithm with other classical methods. The results validate the capability of this algorithm to improve navigation accuracy and achieve the online realization of complex stochastic models.

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Section: Methodsmentioning

confidence: 99%

An Algorithm for Online Stochastic Error Modeling of Inertial Sensors in Urban Cities

Zhao

2023

Sensors

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“…Equation (10) shows derived releasing and capturing bounds, which are used in the following to extract power spectrum density (PSD) and model the LFN. The noise PSD based on carriers number fluctuations [18] is expressed by…”

Section: Releasing and Capturing Boundsmentioning

confidence: 99%

“…Lines 7 to 13 are the release process in which the releasing probability is calculated for each carrier. If this probability is greater than the probability of Equation (10), it means the carrier has been released.…”

Section: Lfn Modelling Using Device-specific and Probabilistic Inputsmentioning

confidence: 99%

“…It is evident that the P t c increases with the number of empty traps. Lines 15 to 20 show the capturing process, in which each free carrier is trapped in an empty trap if the capturing probability is higher than Equation (10), and the N c (t ) would be increased. Finally, in line 22, the PSD of ΔN (t ) is extracted based on f a .…”

Section: Lfn Modelling Using Device-specific and Probabilistic Inputsmentioning

confidence: 99%

“…The LFN research is divided into five major categories: (1) analyzing and modelling LFN [10], (2) utilizing a suitable setup and circuit for the measurement of LFN [11], (3) the design of the circuits with low noise [12], (4) identifying the LFN effect on the performance of different systems [13], and (5) reducing the LFN in various systems [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A near‐DC measurement and modelling of low‐frequency noise in electronic components

Shamaee,

Mivehchy,

Kazemi

2023

IET Science Measure & Tech

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Low‐frequency noise, generated inherently by the number or mobility fluctuation of carriers, is a crucial concern for the design of analog and digital circuits. Unified modelling based on experimental validation of near‐DC noise in amplifiers is a long‐standing open problem. This article develops a model for low‐frequency noise by deriving new bounds for carrier capturing and releasing. According to the proposed model, a measurement system is suggested that operates in a wide frequency range and even at very low frequencies. The system is noise‐tolerant, since the amplifier is selected based on acceptable noise levels. Among the advantages are the independence from specialized structural noise models for each component and the low cost of the measurement system. The evaluation results show that the proposed method leads to a promising improvement in the low‐frequency noise measuring and is superior to conventional models in the normalized root mean square error indicator. Findings reveal that the proposed measurement method can estimate the flicker noise around the DC frequency, and the proposed model agrees reasonably with the proposed measurement circuit.

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Characterized environmental influences on autocollimator measurement uncertainty using an extended Allan variance

Zheng,

Zhang,

et al. 2024

Optics and Lasers in Engineering

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Statistical modelling of rate gyros based on fully overlapping Allan variance

Cited by 7 publications

References 10 publications

An Algorithm for Online Stochastic Error Modeling of Inertial Sensors in Urban Cities

An Algorithm for Online Stochastic Error Modeling of Inertial Sensors in Urban Cities

A near‐DC measurement and modelling of low‐frequency noise in electronic components

Characterized environmental influences on autocollimator measurement uncertainty using an extended Allan variance

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