An improved adaptive Kalman filter for denoising fiber optic gyro drift signal

Narasimhappa, Mundla; Sabat, Samrat L.; Rangababu, P.; Nayak, Jagannath

doi:10.1109/indcon.2013.6725994

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…As an efficient and recursive estimator, Kalman filter has been widely used for eliminating the random noise of FOG sensor [ 18 , 25 , 28 , 31 ]. It is a set of mathematical equations to estimate the state of system and minimize the mean squared error of residuals using the prior knowledge about dynamic process and measurement models, in addition to the process and measurement noise.…”

Section: Adaptive Kalman Filteringmentioning

confidence: 99%

“…To improve the practicability and to avoid divergent effects, adaptive KF (AKF) methods have been investigated which are based on innovation-based adaptive estimation (IAE) or residual-based adaptive estimation (RAE) [ 17 , 18 , 19 ]. An AKF with double transitive factors is developed in [ 17 , 18 ], where the covariance matrix of predicted state vector is modified by an adaptive factor in stage one and the covariance matrix of measurement noise is modified by another adaptive factor in stage two. However, this method requires that innovation or residual vectors at each time point be in the identical type, spatial dimension and distribution, which is difficult to satisfy in a highly dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Adaptive Kalman Filteringmentioning

confidence: 99%

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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“…In this regard, scholars have carried out a lot of research work, and IFOG stochastic error suppression methods based on low-pass filtering (LPF), wavelet transform (WT), empirical mode decomposition (EMD), Kalman filtering (KF), etc, have been proposed one after another [6][7][8][9][10]. However, LPF, WT, EMD and other methods are not applicable to low-frequency noise analysis (long-time error) [11], while the WT and EMD methods are too complex and difficult to implement for the limited computational resources of SINS, and the real-time performance of the algorithms is not good [12]. As a time-domain filtering method, KF, due to its recursive optimal estimation, has more advantages in applications with high real-time demands and limited computational resources [13,14].…”

Section: Introductionmentioning

confidence: 99%

Research on stochastic error suppression method of FOG based on quasi-proportional information

Liang,

Zhou,

et al. 2024

Meas. Sci. Technol.

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In order to improve the precision of FOG, it is necessary to suppress the stochastic error effectively. Therefore, the stochastic error suppression of FOG is studied. Firstly, the data of FOG at room temperature and variable temperature are analyzed. Then, a general form of FOG random error model is derived, and an adaptive filtering method based on quasi-proportional information is proposed to estimate the process noise covariance effectively. At last, the room temperature experiment, the variable temperature experiment and the dynamic experiment are carried out. Compared with the recently proposed adaptive filter algorithm, the variance reduction of the proposed algorithm at the average time of 33s, 54s and 55s is more than 70%, and the suppression effect of the algorithm on the random walk coefficient is improved by more than 2%, showing strong robustness.

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“…The conventional Kalman Filter entails fixed process noise and measurement noise covariance matrix Q and R. The prior knowledge of noise statistics is not enough to represent process noise covariance matrix of a dynamic system precisely. Thus fixed noise covariance can lead to divergent problems [7]. Apart from Kalman Filter methods, SVM(support vector machine) [8] and neural network method [9] are also used to deal with the random errors of MEMS inertial sensors.…”

Section: Introductionmentioning

confidence: 99%

A New Approach in Reducing MEMS Inertial Gyro Random Errors Using Particle Filter

Hu¹

2019

IJMMM

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The accuracy of MEMS inertial sensors is affected by random errors. Kalman Filter is the commonly used approach in reducing the random errors of MEMS sensor output. However, this approach is restricted by some dissatisfaction e.g. the divergent problem and the fixed noise covariance matrix Q and R's inability to represent the dynamic noise characteristics of the system. In this paper, Particle filtering method is employed to reduce the random errors of MEMS gyro output. By a set of samples, Particle Filter is able to represent the posterior distribution of the states in a dynamic system when partial observations are made and random perturbations are present in sensor outputs. Experiments with artificial data sequence as well as authentic ADIS16445 inertial gyro output are conducted and then Particle Filter and Kalman Filter method are introduced to process these signals. Allan Variance analysis reveals that two main random errors are notably diminished after both filtering method and noticeably Particle Filter is superior to Kalman Filter in reducing MEMS gyro random noise.Index Terms-MEMS gyro, random error, Particle Filter, Kalman Filter.

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An improved adaptive Kalman filter for denoising fiber optic gyro drift signal

Cited by 7 publications

References 13 publications

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

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

Research on stochastic error suppression method of FOG based on quasi-proportional information

A New Approach in Reducing MEMS Inertial Gyro Random Errors Using Particle Filter

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