Adaptive Kalman filter based on random-weighting estimation for denoising the fiber-optic gyroscope drift signal

Song, Ningfang; Yuan, Zhengguo; Pan, Xiong

doi:10.1364/ao.58.009505

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…The divergence of the classical KF is restrained, thus reducing the randomness and error of the FOG, and meanwhile improving the accuracy of the FOG. The predicted mean square error of the originally designed KF is changed to Equation (22).…”

Section: Adaptive Kalman Filteringmentioning

confidence: 99%

“…Wang et al [ 21 ] displayed a novel denoising method based on an improved EMD and modified recursive least squares (RLS) algorithm. The results showed that the error mean was reduced by 27.01%, and the horizontal position error was reduced by 106.75 m when the INS lasted for 1000 s. Song et al [ 22 ] described an improved AKF based on innovation and random-weighting estimation (RWE). The quantitative results revealed that the proposed algorithm is competitive for denoising IFOG signals compared with conventional KF, RWE-based gain-adjusted adaptive KF, and RWE-based moving average double-factor adaptive KF.…”

Section: Introductionmentioning

confidence: 99%

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Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method

Wang,

Cui,

Cao

2023

Micromachines

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This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from −30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10−3 to 1.0132 × 10−4, the factor of bias instability (B) reduces from 1.53 × 10−2 to 1 × 10−3, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10−4 to 7.2110 × 10−6. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy.

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Section: Adaptive Kalman Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method

Wang,

Cui,

Cao

2023

Micromachines

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“…Gu [3] conducted experiments based on an improved CEEMDAN (Complete ensemble EMD with adaptive noise)-Bagging ELM compensation method which can enhance generalization performance and boost compensation accuracy of the model, and it was found that the bias instability reduced from 0.0785 • /s to 0.0046 • /s. Also there are other proposed methods to solve the temperature drift of MEMS gyroscopes such as Radial basis function neural network (RBF-NN), Back propagation neural network (BP-NN), Kalman filter, Wavelet threshold denoising, Support vector machine (SVM) [4][5][6][7][8][9][10]. These studies mainly focused on how to reduce the temperature drift of MEMS gyroscopes, however, the detailed influence of humidity on MEMS gyroscopes is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

Cao

Liu

Liu³

et al. 2021

IEEE Access

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To eliminate the humidity drift of gyroscope, a new fusion algorithm (IAWTD-CSVM-EEMD) based on Improved Adaptive Wavelet Threshold De-noising (IAWTD), C-means Support Vector Machine (CSVM) and Ensemble Empirical Mode Decomposition (EEMD) is proposed in this paper. In the new fusion algorithm, the humidity drift component is obtained by denoising the output signal of MEMS gyroscope by IAWTD. Then, the humidity drift compensation model is established, the relative humidity, the change rate of relative humidity and the humidity drift component are taken as the input of the compensation model, and the final gyroscope output can be obtained by using EEMD to denoise the output of compensation model. To verify the compensation effect of the fusion algorithm, the gyroscope outputs are collected and analyzed with the relative humidity ranged from 40% to 90% based on the temperature varying from 20℃ to 60℃. The results show that the IAWTD-CSVM-EEMD method significantly reduces the influence of relative humidity drift on the gyroscope output, according to the quantitative analysis of Allan variance, the quantization noise of the gyroscope output decreases by 87.78%, 96.37%, 97.77%, 99.17% and 92.62% respectively under the relative humidity ranging from 40% to 90%, as the temperature rose from 20 ℃ to 60 ℃ at intervals of 10 ℃, In addition, the bias stability decreases by 96.9%, 99.41%, 99.1%, 99.46%, and 99.78% respectively and the angle random walk decreases by 88.16%, 96.54%, 98.16%, 94.43%, and 92.05% respectively at different temperatures. It is worth mentioning that, to further verify the applicability of the fusion algorithm, a group of comparative experiments are added to consider the influence of temperature changes on the gyroscope output under different relative humidity. The experimental results show that the quantization noise, bias stability and angle random walk of the MEMS gyroscope are significantly reduced compared with the original output after processing by IAWTD-CSVM-EEMD. Therefore, the method proposed in this paper is beneficial to reduce the humidity drift in the MEMS gyroscope output.

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“…Although LiDAR is a unique sensor with several advantages over the traditional inertial sensors, the basic principles of how LiDAR SLAM works in localization are similar to other sensors. Kalman filter (KF) and its variants are usually employed to estimate the drift error in inertial sensors such as IMU [18]- [20], MEMS IMU [21], [22], and fiber optic gyroscope [23]- [25]. After the identification and estimation of drift error, the compensation method can be implemented accordingly.…”

Section: Introductionmentioning

confidence: 99%

The Identification and Compensation of Static Drift Induced by External Disturbances for LiDAR SLAM

Zhou

Wang

et al. 2021

IEEE Access

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With the extending use of LiDAR SLAM in various areas, the interference of external disturbances on SLAM is becoming more and more obvious. Huge efforts have been made to reduce the drift error of LiDAR SLAM using graph-based methods. However, the mapping results can be severely affected by external disturbances under extreme conditions, which will limit the performance of graphbased methods. This study proposes a new strategy to reduce the static drift on a local scale by identifying and compensating the influence of external disturbances based on the localization results of LiDAR SLAM. Contrast experiments were first designed and performed to analyze the potential inducing factors of static drift, such as environment and vibration. The Kalman filter was adopted to estimate the speed and acceleration parameters based on the localization results of LiDAR SLAM. Then, an estimation criterion of static drift was established according to the interference of external disturbances on speed and acceleration. Finally, a static drift compensation method for LiDAR SLAM was proposed to compensate the drift of the pose. In the verification experiment, for 1866 data points, the identification accuracy of static drift was 97.32%, and the final positioning error of LiDAR SLAM was reduced from 4.9464 m to 0.1741 m after the compensation of static drift.

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Adaptive Kalman filter based on random-weighting estimation for denoising the fiber-optic gyroscope drift signal

Cited by 9 publications

References 20 publications

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method

Humidity Drift Modeling and Compensation of MEMS Gyroscope Based on IAWTD-CSVM-EEMD Algorithms

The Identification and Compensation of Static Drift Induced by External Disturbances for LiDAR SLAM

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