A Kalman filter approach based on random drift data of Fiber Optic Gyro

Wu, Xiaofeng; Li, Duan; Chen, Weihai

doi:10.1109/iciea.2011.5975908

Cited by 18 publications

(8 citation statements)

References 9 publications

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“…Moreover, wavelet de-noising takes advantage of the sub-band decomposition performed by the DWT and removes the noise by eliminating the frequency components that are less relevant; in general, this procedure is called wavelet de-nosing and is well described in [3,35,42,43]. …”

Section: Identifying and Extracting Stochastic Model Parametersmentioning

confidence: 99%

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A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Quinchia

Falco

Falletti

et al. 2013

Sensors

162

100

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Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

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Section: Identifying and Extracting Stochastic Model Parametersmentioning

confidence: 99%

“…For static drift data of the inertial sensors, the approximation part of the DWT includes the earth gravity, the earth rotation rate frequency components and the long-term error, while the detail part of the DWT contains the high-frequency noise and other disturbances [5,43]. …”

Section: Experimental Analysismentioning

confidence: 99%

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Quinchia

Falco

Falletti

et al. 2013

Sensors

162

100

View full text Add to dashboard Cite

show abstract

“…• Linear or single models These are usually signal analyzing or filtering methods, such as modeling of time series [9], Kalman filtering (KF) (including modified KFs) [10][11][12] and wavelet threshold filtering [13], etc. In [11], Yi proposed a robust Kalman filtering under model uncertainly.…”

Section: Introductionmentioning

confidence: 99%

A Bias Drift Suppression Method Based on ICELMD and ARMA-KF for MEMS Gyros

Feng

Guo

et al. 2022

Micromachines

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The applications of Micro-Electro-Mechanical-System (MEMS) gyros in inertial navigation system is gradually increasing. However, the random drift of gyro deteriorates the system performance which restricting the applications of high precision. We propose a bias drift compensation model based on two-fold Interpolated Complementary Ensemble Local Mean Decomposition (ICELMD) and autoregressive moving average-Kalman filtering (ARMA-KF). We modify CELMD into ICELMD, which is less complicated and overcomes the endpoint effect. Further, the ICELMD is combined with ARMA-KF to separate and simplify the preprocessed signal, resulting improved denoising performance. In the model, the abnormal noise is removed in preprocess by 2σ criterion with ICELMD. Then, continuous mean square error (CMSE) and Permutation Entropy (PE) are both applied to categorize the preprocessed signal into noise, mixed and useful components. After abandon the noise components and denoise the mixed components by ARMA-KF, we rebuild the noise suppression signal of MEMS gyro. Experiments are carried out to validate the proposed algorithm. The angle random walk of gyro decreases from 2.4156∘/h to 0.0487∘/h, the zero bias instability lowered from 0.3753∘/h to 0.0509∘/h. Further, the standard deviation and the variance are greatly reduced, indicating that the proposed method has better suppression effect, stability and adaptability.

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“…Autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) have been developed in modeling stochastic models for FOG random errors in [ 12 , 13 , 14 ]. Combining these stochastic models, a conventional Kalman filter (CKF) is usually employed to remove the FOG random drift [ 13 , 14 , 15 , 16 ], where the process and measurement noises are pre-calculated by sampling lots of drift data. However, fixed noise variances are unsuitable in real applications which may lead to divergent problems.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Liu

et al. 2015

Sensors

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An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.

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A Kalman filter approach based on random drift data of Fiber Optic Gyro

Cited by 18 publications

References 9 publications

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

A Bias Drift Suppression Method Based on ICELMD and ARMA-KF for MEMS Gyros

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

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