AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal

Yang, Ge; Liu, Yuanyuan; Li, Ming; Shunguang, Song

doi:10.3390/s151026940

Cited by 20 publications

(20 citation statements)

References 29 publications

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“…According to Equation ( 31 ), in order to improve the azimuth measurement precision, the following procedures should be conducted: Constrain the gyro bias error and the gyro random error. Regarding to the former, on one hand, selecting a high precision gyro usually attains a low bias error; on the other hand, using filtering approaches [ 20 , 24 , 32 ] is an alternative method to reduce the gyro bias error. For the gyro random error, a simple and effective method is to average a large number of gyro outputs at each rotation position.…”

Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

“…For the gyro random error, a simple and effective method is to average a large number of gyro outputs at each rotation position. Other filtering approaches can be found in [ 21 , 22 , 24 , 25 ] . Increase the number of measurement points (ensure that the north finding time is less than the time scale of the bias instability [ 4 ]).…”

Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

“…Recently, the structures of different gyros have been comprehensively analyzed to constrain the gyros’ bias error, scale-factor error and random error, e.g., MEMS gyros [ 6 , 7 , 8 , 9 ], ring laser gyros [ 10 , 11 ], fiber optic gyros [ 10 , 12 ], resonant optical gyros [ 13 , 14 , 15 , 16 ] and dynamically tuned gyros [ 17 , 18 , 19 ]. Other very active areas include constraining the gyro bias error and modeling of temperature drift through different kinds of filter methods [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. However, only few works present the details of building a complete gyro north finder system that provides high precision azimuth reference information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

Luo

Wang

Shen

et al. 2016

Sensors

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Even when the Global Positioning System (GPS) signal is blocked, a rate gyroscope (gyro) north finder is capable of providing the required azimuth reference information to a certain extent. In order to measure the azimuth between the observer and the north direction very accurately, we propose a multi-position non-continuous rotation gyro north finding scheme. Our new generalized mathematical model analyzes the elements that affect the azimuth measurement precision and can thus provide high precision azimuth reference information. Based on the gyro’s principle of detecting a projection of the earth rotation rate on its sensitive axis and the proposed north finding scheme, we are able to deduct an accurate mathematical model of the gyro outputs against azimuth with the gyro and shaft misalignments. Combining the gyro outputs model and the theory of propagation of uncertainty, some approaches to optimize north finding are provided, including reducing the gyro bias error, constraining the gyro random error, increasing the number of rotation points, improving rotation angle measurement precision, decreasing the gyro and the shaft misalignment angles. According them, a north finder setup is built and the azimuth uncertainty of 18” is obtained. This paper provides systematic theory for analyzing the details of the gyro north finder scheme from simulation to implementation. The proposed theory can guide both applied researchers in academia and advanced practitioners in industry for designing high precision robust north finder based on different types of rate gyroscopes.

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Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

Section: Estimate Azimuth Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

Luo

Wang

Shen

et al. 2016

Sensors

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“…In the recent years, MEMS devices have been developed and tested successfully for low-end accuracy applications [12,13]. MEMS sensor operates for a long time under poor condition and it generates the noise due to internal circuits and electronics interferences of the MEMS sensor [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Inertial Rate Sensor Errors Using Autoregressive and Moving Average (ARMA) Models

Narasimhappa¹

2020

Gyroscopes - Principles and Applications

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In this chapter, a low-cost micro electro mechanical systems (MEMS) gyroscope drift is modeled by time series model, namely, autoregressive-moving-average (ARMA). The optimality of ARMA (2, 1) model is identified by using minimum values of the Akaike information criteria (AIC). In addition, the ARMA model based Sage-Husa adaptive fading Kalman filter algorithm (SHAFKF) is proposed for minimizing the drift and random noise of MEMS gyroscope signal. The suggested algorithm is explained in two stages: (i) an adaptive transitive factor (a 1) is introduced into a predicted state error covariance for adaption. (ii) The measurement noise covariance matrix is updated by another transitive factor (a 2). The proposed algorithm is applied to MEMS gyroscope signals for reducing the drift and random noise in a static condition at room temperature. The Allan variance (AV) analysis is used to identify and quantify the random noise sources of MEMS gyro signal. The performance of the suggested algorithm is analyzed using AV for static signal. The experimental results demonstrate that the proposed algorithm performs better than CKF and a single transitive factor based adaptive SHFKF algorithm for reducing the drift and random noise in the static condition.

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“…For gyroscopes in practical engineering application, the general requirement is wide range of working temperature. So it is necessary to establish mathematical model to compensate FOG bias data at full temperature [4,5,6]. Different the previous model, time delay between the measured temperature and the actual temperature of the FOG's sensing unit is taken into account in this paper.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Time Delay in Modeling and Compensation of Temperature Error for FOG

Wei¹,

Liu²,

Yang³

et al. 2017

dteees

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Abstract. In order to improve the precision of fiber optic gyroscope (FOG), multiple linear regression has been widely applied into identification and compensation of FOG bias drift causing by temperature variation. There exist inconsistencies between the measured temperature and the actual temperature of the FOG's sensing unit. In this paper, the time delay between FOG bias data and temperature signal is considered before modeling the multiple linear regression for FOG. Then, by using FOG data collected at different time, we can establish more than one regression model. After that, these regression parameters are smoothed to obtain a comprehensive model, which has good parametric adaptability. Experimental results, on periodic and nonperiodic temperature cycling, show the superior performance of this proposed method in compensation of FOG temperature drift and improvement of FOG accuracy. The bias stability of FOG is superior to 0.01°/h at full temperature range -40˚C ~ +60˚C with temperature change rate -1˚C/min ~ +1˚C/min.

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AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal

Cited by 20 publications

References 29 publications

Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

Modeling and Implementation of Multi-Position Non-Continuous Rotation Gyroscope North Finder

Modeling of Inertial Rate Sensor Errors Using Autoregressive and Moving Average (ARMA) Models

Analysis of Time Delay in Modeling and Compensation of Temperature Error for FOG

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