Application of EMD Threshold Filtering for Fiber Optical Gyro Drift Signal De-Noising

Bingbo, 崔冰波 Cui; Xiyuan, 陈熙源 Chen; Rui, 宋锐 Song

doi:10.3788/aos201535.0207001

Cited by 6 publications

(2 citation statements)

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“…The methods, which are based on empirical mode decomposition (EMD) and wavelet decomposition, have good effects on filtering and denoising of nonlinear and non-stationary signals because of their dynamic adaptability. Cui et al [7] improved the EMD threshold filtering method and applied to the drift signal denoising of FOG to obtain stable output. Dong et al [8] proposed a wavelet adaptive threshold full-frequency denoising method, and it had good denoising effect for complex mixed noise scenes.…”

Section: Introductionmentioning

confidence: 99%

Combined denoising method of acceleration signal during mobile robot collision

Chen,

Tang,

Zhong

2023

J. Phys.: Conf. Ser.

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Collision safety of mobile robot is one of the important indexes of robot safety evaluation. The acceleration signal of the robot can completely feed-back the state of the collision process, but it is also vulnerable to noise interference such as vibration and impact, which is difficult to be used for direct analysis. In order to solve the problem of mixing acceleration signal with impact noise and periodic vibration noise during robot collision, a combined filtering noise reduction method based on empirical mode decomposition (EMD), improved wavelet threshold method and dynamic sliding window is proposed. And a wavelet adaptive threshold function is constructed. The wavelet coefficients are automatically adjusted using the information of decomposition degree and noise level, and a dynamic adjustment strategy of sliding window length is designed. The acceleration information of robot can be effectively extracted in the process of strong noise collision. The experimental results show that this method has better noise reduction effect than other methods.

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Section: Introductionmentioning

confidence: 99%

Combined denoising method of acceleration signal during mobile robot collision

Chen,

Tang,

Zhong

2023

J. Phys.: Conf. Ser.

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“…Traditional gyroscope noise reduction methods include Kalman filter [ 7 ], Fast Fourier Transform [ 8 ], Empirical Mode Decomposition [ 9 ], Wavelet Transform [ 10 ], Variational Mode Decomposition [ 11 ], and Ensemble Empirical Mode Decomposition [ 12 ], etc. For example, Liu, Fuchao [ 13 ] proposed an adaptive unscented Kalman filter algorithm by analyzing the influence of the MEMS IMU noise statistical characteristics on the accuracy of the angular rate solution of a high-rotating projectile and verified that the algorithm has better performance than the unscented Kalman filter algorithm with higher estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

Huo

Wang

Shen

et al. 2022

Sensors

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Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.

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Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method

Shu-wen

Qing-qu

et al. 2020

Measurement

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Application of EMD Threshold Filtering for Fiber Optical Gyro Drift Signal De-Noising

Cited by 6 publications

References 0 publications

Combined denoising method of acceleration signal during mobile robot collision

Combined denoising method of acceleration signal during mobile robot collision

Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method

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