MEMS gyroscope wavelet de-noising method based on redundancy and sparse representation

Song, Jinlong; Shi, Zhao; Du, Binhan; Han, Lanyi; Wang, Huaiguang; Wang, Zhiwei

doi:10.1016/j.mee.2019.111112

Cited by 11 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Euler angles, navigation states). [18] [19]- [21] Fuzzy logic [22], [23] Savitsky-Golay [24] [25], [26] [27] Wavelets [28], [29] [30] [31]- [33] [34], [35] [36], [37] Moving average (MA) techniques can be used as efficient smoothing filter, based on errors (residuals) from previous forecasts [6]. Other works elaborated this by combining a weighted regression term over the lagged values, namely autoregressive moving-average (ARMA) [7]- [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Denoising of Accelerometer Signals

Engelsman¹,

Klein²

2022

Preprint

View full text Add to dashboard Cite

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude. Both our code and dataset are publicly available @ GitHub.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Denoising of Accelerometer Signals

Engelsman¹,

Klein²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, several adaptive signal analyzing methods have been studied and applied for MEMS gyros [ 14 , 15 , 16 , 17 , 18 , 19 ], i.e., Empirical Mode Decomposition (EMD) and Local Mean Decomposition (LMD). EMD decomposes the signal into components called intrinsic mode function (IMF), while LMD decomposes the signal into components called product function (PF).…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Guo

et al. 2022

Micromachines

View full text Add to dashboard Cite

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.

show abstract

“…The experimental results show that the proposed method is feasible and could achieve a better performance than the typical mode reversal. Song et al [22] established a real-time wavelet denoising method used for the error compensation of MEMS gyroscopes, and the results show that the 1σ standard deviation of the residual signal is 0.0243 • /s, and the 1σ standard deviation of the residual signal is 0.0175 • /s after the noise reduction by the proposed method. Ding et al [23] proposed an improved variational mode decomposition-wavelet threshold denoising (WTD) method to enhance the performance of MEMS gyroscopes.…”

Section: Introductionmentioning

confidence: 99%

Improved VMD-ELM Algorithm for MEMS Gyroscope of Temperature Compensation Model Based on CNN-LSTM and PSO-SVM

Wang

Cao

2022

Micromachines

View full text Add to dashboard Cite

The micro-electro-mechanical system (MEMS) gyroscope is a micro-mechanical gyroscope with low cost, small volume, and good reliability. The working principle of the MEMS gyroscope, which is achieved through Coriolis, is different from traditional gyroscopes. The MEMS gyroscope has been widely used in the fields of micro-inertia navigation systems, military, automotive, consumer electronics, mobile applications, robots, industrial, medical, and other fields in micro-inertia navigation systems because of its advantages of small volume, good performance, and low price. The material characteristics of the MEMS gyroscope is very significant for its data output, and the temperature determines its accuracy and limits its further application. In order to eliminate the effect of temperature, the MEMS gyroscope needs to be compensated to improve its accuracy. This study proposed an improved variational modal decomposition—extreme learning machine (VMD-ELM) algorithm based on convolutional neural networks—long short-term memory (CNN-LSTM) and particle swarm optimization—support vector machines (PSO-SVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and the gyro output signal with better accuracy is obtained. The VMD algorithm separates the gyro output signal and divides the gyro output signal into low-frequency signals, mid-frequency signals, and high-frequency signals according to the different signal frequencies. Once again, the PSO-SVM model is constructed by the mid-frequency temperature signal to find the temperature error. Finally, the signal is reconstructed through the ELM neural network algorithm, and then, the gyro output signal after noise is obtained. Experimental results show that, by using the improved method, the output of the MEMS gyroscope ranging from −40 to 60 °C reduced, and the temperature drift dramatically declined. For example, the factor of quantization noise (Q) reduced from 1.2419 × 10−4 to 1.0533 × 10−6, the factor of bias instability (B) reduced from 0.0087 to 1.8772 × 10−4, and the factor of random walk of angular velocity (N) reduced from 2.0978 × 10−5 to 1.4985 × 10−6. Furthermore, the output of the MEMS gyroscope ranging from 60 to −40 °C reduced. The factor of Q reduced from 2.9808 × 10−4 to 2.4430 × 10−6, the factor of B reduced from 0.0145 to 7.2426 × 10−4, and the factor of N reduced from 4.5072 × 10−5 to 1.0523 × 10−5. The improved algorithm can be adopted to denoise the output signal of the MEMS gyroscope to improve its accuracy.

show abstract

MEMS gyroscope wavelet de-noising method based on redundancy and sparse representation

Cited by 11 publications

References 15 publications

Data-Driven Denoising of Accelerometer Signals

Data-Driven Denoising of Accelerometer Signals

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

Improved VMD-ELM Algorithm for MEMS Gyroscope of Temperature Compensation Model Based on CNN-LSTM and PSO-SVM

Contact Info

Product

Resources

About