Denoising Method of MEMS Gyroscope Based on Interval Empirical Mode Decomposition

Yang, Liu; Guangwu, Chen; Zong-shou, Wei; Yang, Juhua; Xing, Dongfeng

doi:10.1155/2020/3019152

Cited by 9 publications

(5 citation statements)

References 20 publications

(13 reference statements)

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“…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%

“…Empirical mode decomposition (EMD) is a time frequency analysis which decomposes multicomponent signals into a finite number of Intrinsic Mode Functions (namely, its building blocks). This way, local characteristic time scale is emphasized such that non-stationary and nonlinear signals can be robustly handled [16]- [21]. In fuzzy logic, quantified statements are used to determine objects level of membership in values ranging from zero to one, instead of true or false.…”

Section: Literature Reviewmentioning

confidence: 99%

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Data-Driven Denoising of Accelerometer Signals

Engelsman¹,

Klein²

2022

Preprint

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Denoising of Accelerometer Signals

Engelsman¹,

Klein²

2022

Preprint

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show abstract

“…Predicted variance decomposition: e variance decomposition [27][28][29][30][31][32][33][34][35] is conducted to attribute the variance of the endogenous variables, to analyze the contribution of each structural impact to its change, and to judge the importance of the impact of different variables. erefore, the variance decomposition of the real estate price index can more clearly explore the importance of the variables affecting it.…”

Section: Model Stationarity Testmentioning

confidence: 99%

The Correlation between Monetary Policy and Housing Price Change Based on a VAR Model

Feng

2022

Mathematical Problems in Engineering

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In early 2020, the global economy was severely hit by the COVID-19 pandemic. Governments, including China, have adopted expansionary fiscal policies to stimulate demand and quantitative easing to increase the money supply to boost the economy by lowering lending rates. This paper starts from the influence of monetary policy on housing price mechanism, tries to explore the monetary policy on interest rate and money supply, finally establishes the VAR model, empirically analyzes the effectiveness of the impact of monetary policy on housing price changes, and makes a policy suggestion to demonstrate the necessity of consideration when stimulating the economy, to avoid housing price rise too fast, which affects the people's livelihood, and to have a negative impact on social sustainable development.

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“…Currently, there are mainly the following methods for noise removal of inertial devices: the time series modeling compensation method, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, the wavelet threshold denoising (WTD) method, neural network compensation and other methods. Reference [3] proposes a compensation method for time series modeling based on empirical mode decomposition(EMD) decomposition, which performs time series modeling on the IMF after EMD decomposition and combines the Kalman filter model for noise reduction processing. Although this scheme can remove part of the noise, it did not significantly enhance the SNR(signalto-noise ratio) of the signal.…”

Section: Introductionmentioning

confidence: 99%

A new signal denoising algorithm for low-cost MEMS gyroscope

Lv,

Si,

et al. 2023

Third International Conference on Sensors and Information Technology (ICSI 2023)

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Inertial measurement unit (IMU) has been commonly used in measuring angles, displacement measurement and other fields. However, micro electro-mechanical system (MEMS) gyroscopes have little accuracy of measuring and enormous noise; real signals will be cloaked in the noise. A new denoising algorithm for low cost MEMS gyroscope is offeredin this paper. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the initial-signal which can obtain a limited number of intrinsic mode functions (IMF). Next, use the power spectrum entropy to divide the IMF into noise signals, mixed signals and sound signals. Then, variational mode decomposition (VMD) is choosen to decompose the mixed signal, and using reptile search algorithm (RSA) optimization algorithm optimizes the crucial arguments of VMD. VMD algorithm appropriately dismantles the signal according to the optimized arguments to attain the IMF sections, divided into signal sections and noise sections using the Bhattacharyian distance. The signal sections are furthermore processed by lifting wavelet threshold denoising (LWT), and the valuable signal with denoising is obtained. Finally, all the valuable signals are amalgamated and reestablished into the endingdenoising signal. The effectuality of the algorithm is weighted by Matlab simulation. The static denoising experimentation of the three-axis turntable is put up in the experimental analysis, which exhaustively confirms that the algorithm has clear superiority in denoising and significantly increases the signal quality and the accuracy of the cheap MEMS gyroscope.

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Denoising Method of MEMS Gyroscope Based on Interval Empirical Mode Decomposition

Cited by 9 publications

References 20 publications

Data-Driven Denoising of Accelerometer Signals

Data-Driven Denoising of Accelerometer Signals

The Correlation between Monetary Policy and Housing Price Change Based on a VAR Model

A new signal denoising algorithm for low-cost MEMS gyroscope

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