Abstract: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 i… Show more
“…The influence of network parameter scale and temperature on the results is also analyzed. As shown in Figure 4, the experimental device includes the inertial sensor ADIS16475 [29] The key part of a neural network for nonlinear error fitting is the activation function, and the sigmoid activation function is commonly used in related works, which has the following expressions [20][21][22][23][24]:…”
Section: Implementation Details and Experimental Resultsmentioning
confidence: 99%
“…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%
“…The accuracy improvement of statistical methods requires accurate analysis and modeling of error components, while another research idea is to obtain model structure and model parameters with machine learning schemes. The researchers use multilayer perception machines [20,21], recurrent neural networks [22][23][24], mixed deep learning networks [25,26], etc. to establish error models.…”
Section: Introductionmentioning
confidence: 99%
“…The nonlinear error in the composition of measurement error is discussed. In related research studies [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], the nonlinear error is modeled as a time series model, in which the error at the current moment is related to the observation values at previous moments. The error compensation scheme discussed in this paper is also based on the analysis of the time series model.…”
Section: Problem Description and Introduction Of Error Compensation Schemementioning
confidence: 99%
“…(a) (b) The key part of a neural network for nonlinear error fitting is the activation function, and the sigmoid activation function is commonly used in related works, which has the following expressions [20][21][22][23][24]:…”
Section: Problem Description and Introduction Of Error Compensation Schemementioning
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.
“…The influence of network parameter scale and temperature on the results is also analyzed. As shown in Figure 4, the experimental device includes the inertial sensor ADIS16475 [29] The key part of a neural network for nonlinear error fitting is the activation function, and the sigmoid activation function is commonly used in related works, which has the following expressions [20][21][22][23][24]:…”
Section: Implementation Details and Experimental Resultsmentioning
confidence: 99%
“…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%
“…The accuracy improvement of statistical methods requires accurate analysis and modeling of error components, while another research idea is to obtain model structure and model parameters with machine learning schemes. The researchers use multilayer perception machines [20,21], recurrent neural networks [22][23][24], mixed deep learning networks [25,26], etc. to establish error models.…”
Section: Introductionmentioning
confidence: 99%
“…The nonlinear error in the composition of measurement error is discussed. In related research studies [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], the nonlinear error is modeled as a time series model, in which the error at the current moment is related to the observation values at previous moments. The error compensation scheme discussed in this paper is also based on the analysis of the time series model.…”
Section: Problem Description and Introduction Of Error Compensation Schemementioning
confidence: 99%
“…(a) (b) The key part of a neural network for nonlinear error fitting is the activation function, and the sigmoid activation function is commonly used in related works, which has the following expressions [20][21][22][23][24]:…”
Section: Problem Description and Introduction Of Error Compensation Schemementioning
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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