Allan variance method for gyro noise analysis using weighted least square algorithm

Lyu, Pin; Liu, Jianye; Lai, Jizhou; Huang, Kai

doi:10.1016/j.ijleo.2015.06.044

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…Lawrence analyzed the performance of ring laser gyroscope based on Allan variance [18]. Pin analyzed the noise characteristics of fiber optic gyroscope based on weighted least squares algorithm and Allan variance [26]. Grantham analyzed the random error of inertial sensors using Allan variance [11].…”

Section: Słowa Kluczowe: żYroskop Mems Naprężenie Cieplne Test Przyspieszonej Degradacji Model Współczynnikamentioning

confidence: 99%

“…where Q is a random drift caused by quantization noise. The initial output of the inertial sensor is analog data, which is quantized into digital data for calculation, thus quantization noise is generated at the output of the inertia device [26]. N is the white noise generated during the measurement process, which appears as an angular random walk at the output of the MEMS gyro and belongs to high frequency noise [19].…”

Section: The Principle Of Allan Variancementioning

confidence: 99%

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Lifetime prediction method for MEMS gyroscope based on accelerated degradation test and acceleration factor model

Liu

Wang

Fan

et al. 2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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Lifetime prediction method for memS gyroScope baSed on acceLerated degradation teSt and acceLeration factor modeL metoda prognozowania czaSu pracy żyroSkopu memS na podStawie teStu przySpieSzonej degradacji i modeLu wSpółczynnika przySpieSzenia The reliability analysis of MEMS gyroscope under long-term operating condition has become an urgent requirement with the enlargement of its application scope and the requirement of good durability. In this study we propose a lifetime prediction method for MEMS gyroscope based on accelerated degradation tests (ADTs) and acceleration factor model. Firstly, the degradation characteristic (bias instability) is extracted based on Allan variance. The effect of temperature stress on the degradation rate of bias instability is analyzed, and it shows that the degradation rate of bias instability would increase with the increase of the temperature. Secondly, the ADTs of MEMS gyroscope are designed and conducted, the degradation model of MEMS gyroscope is established based on the output voltage of MEMS gyroscope and Allan variance. Finally, the acceleration factor model of MEMS gyroscope under temperature stress is derived, and the lifetime of the MEMS gyroscope is predicted based on two group tests data under high stress level. The results show that the lifetime calculated by the acceleration factor model and mean lifetime under high stress levels is close to the mean lifetime calculated by the linear equation at normal temperature stress.

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Section: Słowa Kluczowe: żYroskop Mems Naprężenie Cieplne Test Przyspieszonej Degradacji Model Współczynnikamentioning

confidence: 99%

Section: The Principle Of Allan Variancementioning

confidence: 99%

Lifetime prediction method for MEMS gyroscope based on accelerated degradation test and acceleration factor model

Liu

Wang

Fan

et al. 2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…Allan Variance Comparison Analysis. Allan variance analysis is an effective method for gyro random error identification and noise characteristics analysis [21]. e method can identify multiple different types of random errors in different time domains and can classify the error term into five error terms, quantization noise, angular random walk, zero offset instability, angular rate random walk, and speed ramp, and the error coefficients can be analyzed quantitatively [22,23].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Denoising Method of MEMS Gyroscope Based on Interval Empirical Mode Decomposition

Yang

Guangwu

Zong-shou

et al. 2020

Mathematical Problems in Engineering

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The microelectromechanical system (MEMS) gyroscope has low measurement accuracy and large output noise; the useful signal is often submerged in the noise. A new denoising method of interval empirical mode decomposition (IEMD) is proposed. Firstly, the traditional EMD algorithm is used to decompose the signal into a finite number of intrinsic mode functions (IMFs). Based on the Bhattacharyya distance analysis and the characteristics of the autocorrelation function, a screening mechanism is proposed to divide IMFs into three categories: noise IMFs, mixed IMFs, and signal IMFs. Then, the traditional modelling filtering method is used to filter the mixed IMFs. Finally, the mixed IMFs after modelling and filtering and signal IMFs are reconstructed to obtain the denoised signal. In the experimental analysis, the static denoising experiment of the turntable, the Allan variance analysis, dynamic denoising experiment, and vehicle experiment are set up in this paper, which fully proves that the method has obvious advantages in denoising and greatly improves the quality of signal and the accuracy of the inertial navigation system solution.

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“…Allan variance [10] is a famous method to characterize and identify various error sources in FOG, as well as their contribution to the statistical properties of the entire noise in detail. It is very easy to obtain types of error sources and the magnitude of each error source, by using the quantitative relationship between Allan variance and the power spectrum density (PSD).…”

Section: Allan Variancementioning

confidence: 99%

“…Generally, the values of p and q are determined by correlation analysis, using an autocorrelation function and a partial correlation function. The parameters of the model are generally determined by the Box-Jenkins method, or the least-squares method [10][11][12]. In the paper, this is done by using an intelligent optimization algorithm.…”

Section: Arima Modelmentioning

confidence: 99%

Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model

Hou

et al. 2019

Algorithms

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As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.

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Allan variance method for gyro noise analysis using weighted least square algorithm

Cited by 12 publications

References 9 publications

Lifetime prediction method for MEMS gyroscope based on accelerated degradation test and acceleration factor model

Lifetime prediction method for MEMS gyroscope based on accelerated degradation test and acceleration factor model

Denoising Method of MEMS Gyroscope Based on Interval Empirical Mode Decomposition

Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model

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