Improved Virtual Gyroscope Technology Based on the ARMA Model

Song, Jinlong; Shi, Zhao; Wang, Lvhua; Wang, Hailiang

doi:10.3390/mi9070348

Cited by 11 publications

(8 citation statements)

References 12 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%

“…The smoothing process entails the extraction of a stable random drift sequence via period-based analysis, with linear autoregressive, sliding average, or mixed autoregressive sliding average utilized in fitting the random drift sequence to obtain a smooth signal. The main operations in autoregressive averaging model are the determination of suitable model structure, identification of the model parameters, and validation of the model’s applicability [ 27 ]. The Allan variance method is a standard analytical approach for describing and identifying the various error sources in a MEMS gyroscope and similarly for statistical characterization of the noise source [ 35 ].…”

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%

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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“…This type of error directly affects the stability of the output signals and is difficult to be processed directly through device calibration [8]. Therefore, the modeling and compensation schemes of the nonlinear error components are widely studied and two mainstream research schemes [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] are formed, namely, (1) establishing a statistical model and performing error compensating and (2) error compensation schemes based on machine learning or deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…Through power spectral density function analysis [12], the nondeterministic error can be modeled as Gaussian white noise or colored noise, and error compensated methods are analyzed [13]. In order to improve the accuracy of modeling, the empirical model decomposition [14], and autoregressive-moving-average (ARMA) time series model [15][16][17] are introduced into the error compensation schemes. Based on the Kalman filtering algorithm, the error compensation scheme can achieve better results and improve the accuracy of the statistical model [18,19].…”

Section: Introductionmentioning

confidence: 99%

Online Nonlinear Error Compensation Circuit Based on Neural Networks

Gao

Zhou

et al. 2021

Machines

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Nonlinear errors of sensor output signals are common in the field of inertial measurement and can be compensated with statistical models or machine learning models. Machine learning solutions with large computational complexity are generally offline or implemented on additional hardware platforms, which are difficult to meet the high integration requirements of microelectromechanical system inertial sensors. This paper explored the feasibility of an online compensation scheme based on neural networks. In the designed solution, a simplified small-scale network is used for modeling, and the peak-to-peak value and standard deviation of the error after compensation are reduced to 17.00% and 16.95%, respectively. Additionally, a compensation circuit is designed based on the simplified modeling scheme. The results show that the circuit compensation effect is consistent with the results of the algorithm experiment. Under SMIC 180 nm complementary metal-oxide semiconductor (CMOS) technology, the circuit has a maximum operating frequency of 96 MHz and an area of 0.19 mm2. When the sampling signal frequency is 800 kHz, the power consumption is only 1.12 mW. This circuit can be used as a component of the measurement and control system on chip (SoC), which meets real-time application scenarios with low power consumption requirements.

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“…In addition, the common random error model of MEMS gyroscopes only considers the rate random walk (RRW) and angle random walk (ARW) ignoring other noise items. Furthermore, MEMS gyroscope’s output signal has a weak linear trend item [ 25 , 26 , 27 ]; (2) the existing fusion method uses the Conventional Kalman Filter (CKF) algorithm [ 28 , 29 , 30 ]. The premise of CKF to obtain the optimal estimation is that the structural parameters and statistical noise parameters of stochastic dynamic systems need to be known accurately [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Roll Angular Rate Measurement for High Spinning Projectiles Based on Redundant Gyroscope System

Zhang

et al. 2020

Micromachines

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Precision-guided projectiles, which can significantly improve the accuracy and efficiency of fire strikes, are on the rise in current military engagements. The accurate measurement of roll angular rate is critical to guide a gun-launched projectile. However, Micro-Electro-Mechanical System (MEMS) gyroscope with low cost and large range cannot meet the requirement of high precision roll angular rate measurement due to the limitation by the current technology level. Aiming at the problem, the optimization-based angular rate estimation (OBARS) method specific for projectiles is proposed in this study. First, the output angular rate model of redundant gyroscope system based on the autoregressive integrated moving average (ARIMA) model is established, and then the conventional random error model is improved with the ARIMA model. After that, a Sage-Husa Adaptive Kalman Filter (SHAKF) algorithm that can suppress the time-varying process and measurement noise under the flight condition of the high dynamic of the projectile is designed for the fusion of dynamic data. Finally, simulations and experiments have been carried out to validate the performance of the method. The results demonstrate the proposed method can effectively improve the angular rate accuracy more than the related traditional methods for high spinning projectiles.

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Improved Virtual Gyroscope Technology Based on the ARMA Model

Cited by 11 publications

References 12 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

Online Nonlinear Error Compensation Circuit Based on Neural Networks

Roll Angular Rate Measurement for High Spinning Projectiles Based on Redundant Gyroscope System

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