A Gyroscope Signal Denoising Method Based on Empirical Mode Decomposition and Signal Reconstruction

Liu, Chenchen; Yang, Zhiqiang; Shi, Z.; Ma, Ji; Cao, Jian

doi:10.3390/s19235064

Cited by 23 publications

(11 citation statements)

References 38 publications

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“…Statistically speaking, the more the white noise is added, the smaller the influence of auxiliary white noise on the decomposition result. The steps of the EEMD algorithm are as follow [18,19]:…”

Section: Iiialgorithms and Model A Ensemble Empirical Mode Decomposition (Eemd)mentioning

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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Section: Iiialgorithms and Model A Ensemble Empirical Mode Decomposition (Eemd)mentioning

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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“…(3) The noise is calculated by the fixed number of the intrinsic mode function (IMF) components in [ 8 ], which is unrealistic. The proposed algorithm uses the power spectral density (PSD) feature of the IMF to select different components to reconstruct the noise signal in different environments, which is adaptive [ 21 ]. Compared with the multi-model adaptive Kalman filter in [ 11 ], the algorithm proposed in this paper only needs one Kalman filter, which will save resources and time for calculation.…”

Section: Introductionmentioning

confidence: 99%

UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

Zhang¹,

Zhou²,

Wang³

2021

Sensors

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Aiming to improve the positioning accuracy of an unmanned aerial vehicle (UAV) swarm under different scenarios, a two-case navigation scheme is proposed and simulated. First, when the Global Navigation Satellite System (GNSS) is available, the inertial navigation system (INS)/GNSS-integrated system based on the Kalman Filter (KF) plays a key role for each UAV in accurate navigation. Considering that Kalman filter’s process noise covariance matrix Q and observation noise covariance matrix R affect the navigation accuracy, this paper proposes a dynamic adaptive Kalman filter (DAKF) which introduces ensemble empirical mode decomposition (EEMD) to determine R and adjust Q adaptively, avoiding the degradation and divergence caused by an unknown or inaccurate noise model. Second, a network navigation algorithm (NNA) is employed when GNSS outages happen and the INS/GNSS-integrated system is not available. Distance information among all UAVs in the swarm is adopted to compensate the INS position errors. Finally, simulations are conducted to validate the effectiveness of the proposed method, results showing that DAKF improves the positioning accuracy of a single UAV by 30–50%, and NNA increases the positioning accuracy of a swarm by 93%.

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“…Liu et al. developed a denoising method combining EMD and interval threshold methods to suppress the random error of FOG signal successfully [ 18 ]. Wang et al.…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

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A Gyroscope Signal Denoising Method Based on Empirical Mode Decomposition and Signal Reconstruction

Cited by 23 publications

References 38 publications

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

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

UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

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