Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

Zhang, Miao; Shen, Feng; Xu, Dingjie; He, Kaifei; Tian, Chunmiao

doi:10.3390/s150202496

Cited by 13 publications

(12 citation statements)

References 22 publications

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“…Power spectral density is a frequency domain method and Allan variance is a time domain method (You-Long et al , 2016), and the two methods are essentially equivalent (Yi, 2007). Compared with the power spectral density method, Allan variance is simple, and it is easier to separate and characterize noise using this method (Miao et al , 2015). Therefore, Allan variance is selected to analyze the gyroscope error in this paper.…”

Section: Stochastic Process Of Heading Driftmentioning

confidence: 99%

Research on shearer positioning with double-INS

Wang

2019

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Purpose This paper aims to improve shearer positioning accuracy. Shearer positioning using an inertial navigation system (INS) is a highly useful technology; however, positioning accuracy is seriously hindered by INS attitude error, particularly heading drift. Design/methodology/approach A shearer positioning model with double-INS based on extended Kalman filter was proposed. The constant distance between two INSs (INS 1 and INS 2) was selected as the observation vector. Allan variance was used to identify the noise type of the vertical-axis gyroscope, and the stochastic process of heading drift for two INSs was obtained and divided into incongruous drift and concurrent drift. Findings Simulation was then carried out to determine the optimal arrangement of the two INSs. For incongruous drift, the optimal arrangement satisfied the condition that the line connecting INS 1 and INS 2 was perpendicular to the shearer lateral axis (in the shearer coordinate frame) and parallel to the east-north plane (in the east-north-up coordinate frame). Under optimal arrangement, the positioning accuracy increased against the distance between INS 1 and INS 2. For concurrent drift, the double-INS positioning model had no effect. Under the circumstances, the number of INSs should be increased so that the uncertainty of INS drift was reflected as much as possible. Originality/value A new double-INS positioning model was proposed with the constant distance between the two INSs. The optimal arrangement for double-INS was obtained.

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Section: Stochastic Process Of Heading Driftmentioning

confidence: 99%

Research on shearer positioning with double-INS

Wang

2019

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“…If the noise sources are independent of each other, then the Allan variance is the sum of squares of each type of error. The Allan variance of FOG can be expressed as [ 27 , 28 , 29 , 30 , 31 ]:

…”

Section: Experiments and Analysis Of Adaptive Filtering Based On Thmentioning

confidence: 99%

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter

Sun

Liu

et al. 2016

Sensors

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In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

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“…The review shows that methods based on the analysis of sample characteristics are widely used in applied problems concerned with the processing of measurement data. Such sample characteristics include power spectral density [10,11], Allan variance (AV) [12,13,14,15,16,17,18], etc. These methods are usually effective only for stationary ergodic processes; in addition, they require long-term measurements.…”

Section: Introductionmentioning

confidence: 99%

Performance Criteria for the Identification of Inertial Sensor Error Models

Степанов

Motorin

2019

Sensors

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This paper considers performance criteria for the identification of sensor error models and the procedure for their calculation. These criteria are used to investigate the efficiency of the identification problem solution, depending on the initial data, and to carry out a comparative analysis of various suboptimal algorithms. The calculation procedure is based on an algorithm that solves the joint problem of hypothesis recognition and parameter estimation within the Bayesian approach. A performance analysis of the models traditionally used to describe errors of inertial sensors is given to illustrate the application of the procedure for the calculation of performance criteria.

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Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

Cited by 13 publications

References 22 publications

Research on shearer positioning with double-INS

Research on shearer positioning with double-INS

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter

Performance Criteria for the Identification of Inertial Sensor Error Models

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