Modeling and compensation of MEMS gyroscope output data based on support vector machine

Zhang, Yanshun; Wang, Yuanyuan

doi:10.1109/icnc.2011.6022022

Cited by 4 publications

(6 citation statements)

References 25 publications

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“…In the last decade, many representative methods emerged for denoising MEMS gyro. These include autoregressive sliding average [ 27 ], Allan variance [ 28 ], Kalman filtering [ 29 ], wavelet thresholding [ 30 ], and machine learning represented by neural network (NN) and support vector machine (SVM) [ 31 , 32 , 33 , 34 ]. Since the output signal of MEMS gyro is generally non-stationary, the original signal needs to be smoothed.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

et al. 2021

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Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

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Section: Introductionmentioning

confidence: 99%

“…The SVM method was initially used for classification but with the introduction of insensitive loss functions, SVM has successfully been extended for regression estimation of nonlinear systems. SVM is applicable for nonlinear processing, thus it is widely used in MEMS sensors error compensation [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

et al. 2021

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“…In MEMS gyroscope error compensation research, the MEMS gyroscope data are generally treated as time-series data. Scholars have proposed methods such as the autoregressive moving average (ARMA) model, the Allan variance (AV), the wavelet threshold (WT), the support vector machine (SVM), and the artificial neural network (ANN), and all of them have achieved excellent results [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Recently, various variants of the recurrent neural network (RNN), which has strong processing power for time-series data, have been shown to be superior to traditional methods in the research of error compensation in MEMS gyroscopes [ 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Zhu

Cai

Yang

et al. 2021

Sensors

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In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

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“…Also, the model development process takes longer time, which limits their real-time implementation [ 16 ]. Recently, Support Vector Machines (SVMs) based techniques have been applied to model the MEMS error [ 17 , 18 ]. Support Vector Machines (SVMs) based on the structural risk minimization principle can avoid local minimization and over-fitting problems as encountered in NN, thus improving the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…This paper thus proposes the implementation of an enhanced Nu-Support Vector Regression (Nu-SVR) technique for modeling these random and substantial MEMS sensor errors [ 19 ]. The proposed approach is different from those presented in [ 17 , 18 ], as it automatically selects the model parameter ( i.e. , error margin), and the priori knowledge of the noise model is not mandatory [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

Bhatt

Aggarwal

Bhattacharya

et al. 2012

Sensors

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Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.

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Modeling and compensation of MEMS gyroscope output data based on support vector machine

Cited by 4 publications

References 25 publications

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

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