scholarly journals An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3943 ◽  
Author(s):  
Yanshun Zhang ◽  
Chuang Peng ◽  
Dong Mou ◽  
Ming Li ◽  
Wei Quan
Keyword(s):  
Adaptive Filtering ◽  
Random Error ◽  
Error Compensation ◽  
Angular Rate ◽  
Micro Electro Mechanical System ◽  
Random Errors ◽  
Variance Model ◽  
Rate Condition ◽  
Mems Gyroscope ◽  
Filtering Approach

To improve the dynamic random error compensation accuracy of the Micro Electro Mechanical System (MEMS) gyroscope at different angular rates, an adaptive filtering approach based on the dynamic variance model was proposed. In this paper, experimental data were utilized to fit the dynamic variance model which describes the nonlinear mapping relations between the MEMS gyroscope output data variance and the input angular rate. After that, the dynamic variance model was applied to online adjustment of the Kalman Filter measurement noise coefficients. The proposed approach suppressed the interference from the angular rate in the filtering results. Dynamic random errors were better estimated and reduced. Turntable experiment results indicated that the adaptive filtering approach compensated for the MEMS gyroscope dynamic random error effectively both in the constant angular rate condition and the continuous changing angular rate condition, thus achieving adaptive dynamic random error compensation.

Random Error Compensation Technology of MEMS Gyroscope Based on Time-varying ARMA Model

2018 ◽  
Vol 47 (7) ◽  
pp. 712003
Author(s):  
宋金龙 SONG Jin-long ◽  
石志勇 SHI Zhi-yong ◽  
王律化 WANG Lü-hua ◽  
王海亮 WANG Hai-liang
Keyword(s):  
Random Error ◽  
Error Compensation ◽  
Arma Model ◽  
Time Varying ◽  
Mems Gyroscope

Modeling Study on Random Error of Fiber Optic Gyro

2012 ◽  
Vol 239-240 ◽  
pp. 167-171
Author(s):  
Fan Zhang
Keyword(s):  
Random Walk ◽  
Random Error ◽  
Fiber Optic ◽  
Angular Rate ◽  
Measurement Unit ◽  
Random Errors ◽  
Fiber Optic Gyro ◽  
Zero Bias ◽  
Instability Rate ◽  
Bias Instability

An accurate modeling method for the random error of the fiber optic gyro (FOG) is presented. Taking the FOG in the inertial measurement unit of one specific inertial navigation system as the subject investigated, the method is composed of the data acquisition, preprocessing, establishing the FOG AR(2) model and performing Kalman filtering based on the model. The filtering result and the Allan variance analysis of FOG prove that the method effectively reduces the FOG random error, decreasing the angle random walk, zero-bias instability, rate random walk, angular rate ramp and quantification noise of FOG signals to less than one half of the corresponding values before the filtering of FOG random errors, which improves the accuracy of FOG.

scholarly journals Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope

Micromachines ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 608 ◽  
Author(s):  
Guangrun Sheng ◽  
Guowei Gao ◽  
Boyuan Zhang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Inertial Navigation ◽  
Micro Electro Mechanical System ◽  
Threshold Function ◽  
Navigation Systems ◽  
Random Errors ◽  
Mems Gyroscope ◽  
Rbf Network ◽  
Major Factors

The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes.

Intelligent and Analog CMOS ASIC Development of Angular Rate Error Compensation for MEMS Gyroscope

2019 ◽  
Vol 9 (3) ◽  
pp. 388-403
Author(s):  
Ramesh Pawase ◽  
Niteen P. Futane
Keyword(s):  
Error Compensation ◽  
Angular Rate ◽  
Back Propagation ◽  
Activation Function ◽  
Compensation Circuit ◽  
Interface Circuits ◽  
Interface Circuit ◽  
Mems Gyroscope ◽  
Analog Cmos ◽  
Hidden Layer

Background & Objective: MEMS-based gyroscopes are used in angular rate detection where precision is an important parameter; however, gyroscope output is limited by angular rate error. For minimizing these types of non-idealities, conventional external hardware-based analog or digital circuits have limitations for using in compact applications. CMOS analog ASIC for angular rate error compensation is necessary as both MEMS-CMOS technologies are supplementary and compatible. Method: In this paper, the output of MEMS gyroscope is taken as input for the compensation circuit which results in compensated angular rate. ANN is used in intelligent compensation circuit for error reduction in which offline data is trained and minimum optimum error of MSE of 1.72e-4 is achieved. ANN uses tanh sigmoidal activation function and back propagation trained MLP model with three neurons in the hidden layer. The equivalent ANN is implemented by CMOS ASIC where each neuron is implemented using Gilbert multiplier cell, differential analog adder, and differential amplifier as tanh sigmoidal circuit using OrCAD-PSpice 10.5 with 0.35 μ m technology. These blocks consist of differential configuration which has the capability of common mode interference rejection as noise becomes comparable at lower values of input analog signal. The entire ASIC consumes 77.8 mW of power which is far less and compact in size as compared to available external hardware interface circuits. Result and Conclusion: MEMS gyroscope with proposed analog ASIC becomes smart sensor with ANN based intelligent interface circuit. The proposed compensation cum interface circuit gives the average angular rate error of 1.91% in the range of minimum 0% to maximum 27% leading to improved accuracy.

The method of MEMS gyroscope random error compensation based on ARMA

2021 ◽  
Author(s):  
Ming Kuan Ding ◽  
Zhiyong Shi ◽  
Binhan Du ◽  
huaiguang wang ◽  
Lanyi Han ◽  
...  
Keyword(s):  
Random Error ◽  
Error Compensation ◽  
Mems Gyroscope

MEMS Gyroscope Random Error Modeling and Filtering

2010 ◽  
Vol 29-32 ◽  
pp. 829-834 ◽  
Author(s):  
Bo Ren ◽  
De Ming Zhang ◽  
Huan Li
Keyword(s):  
Real Time ◽  
Random Error ◽  
High Reliability ◽  
Low Cost ◽  
Random Sequence ◽  
Engineering Application ◽  
Error Modeling ◽  
Ar Model ◽  
Random Errors ◽  
Mems Gyroscope

MEMS gyroscope is a new type of inertial device with small size, low cost, light weight, high reliability, but less precise and random error is relatively large. In this paper, from a practical engineering application point of view, first, the MEMS gyroscope random errors is real-time average filtered. Then, based on the basic principle of time series analysis of random sequence , the first-order AR model of MEMS gyroscope random errors is established. Finally, based on Markov characteristic of kalman filtering algorithm, each output of the MEMS gyroscope is multiple real-time filtered. Through the specific data processing, MEMS gyroscope random errors reduced to about two per cent of the original.

Research on MEMS Gyroscope Random Error Compensation Algorithm Based on ARMA Model

2014 ◽  
Vol 602-605 ◽  
pp. 891-894 ◽  
Author(s):  
Ming Ming Chen ◽  
Guo Wei Gao
Keyword(s):  
Random Error ◽  
Error Compensation ◽  
Measurement Accuracy ◽  
High Reliability ◽  
Arma Model ◽  
Mems Devices ◽  
Measuring Device ◽  
Mems Gyroscope ◽  
Compensation Algorithm ◽  
Dynamic Level

. MEMS device based on MEMS technology has the advantages of small volume, light weight, low cost, shock resistance, high reliability, it is widely used in the dynamic level measuring device. But due to the interference of external environment, the measurement accuracy of MEMS devices has been difficult to achieve practical application level. This paper analyzes the factors influencing the measurement accuracy of MEMS devices in the dynamic level measurement, is proposed based on the improved MEMS gyro random error compensation algorithm for ARMA model. Processed by the random error of a certain type of gyro, the test, the measuring accuracy of MEMS gyroscope has been significantly improved in the before and after filtering. After Kalman the improved filter and Kalman filter adaptive fading factor is introduced, in the static condition, the standard error of the difference of the original error are reduced to 3.75% and 4.8%, the filtering precision and dynamic environment is also effectively improved. Prove that the method is feasible and effective and is of great practical significance.

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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1181
Author(s):  
Chenhao Zhu ◽  
Sheng Cai ◽  
Yifan Yang ◽  
Wei Xu ◽  
Honghai Shen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Standard Deviation ◽  
Error Compensation ◽  
Random Vibration ◽  
Short Term Memory ◽  
Combined Method ◽  
Short Term ◽  
Mems Gyroscope ◽  
Long Short Term Memory ◽  
Lstm Network

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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