scholarly journals Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

Micromachines ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 266-280 ◽  
Author(s):  
Liang Xue ◽  
Chengyu Jiang ◽  
Lixin Wang ◽  
Jieyu Liu ◽  
Weizheng Yuan
Keyword(s):  
Noise Reduction ◽  
Angular Rate ◽  
Mems Gyroscope ◽  
Direct Modeling

scholarly journals A Noise Reduction Method for MEMS Gyroscope Based on Direct Modeling and Kalman Filter

2018 ◽  
Vol 51 (31) ◽  
pp. 172-176 ◽  
Author(s):  
Shuo Cai ◽  
Yunfeng Hu ◽  
Haitao Ding ◽  
Hong Chen
Keyword(s):  
Kalman Filter ◽  
Noise Reduction ◽  
Reduction Method ◽  
Mems Gyroscope ◽  
Direct Modeling

scholarly journals Analysis of Correlation in MEMS Gyroscope Array and its Influence on Accuracy Improvement for the Combined Angular Rate Signal

Micromachines ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 22 ◽  
Author(s):  
Liang Xue ◽  
Xinguo Wang ◽  
Bo Yang ◽  
Weizheng Yuan ◽  
Guangmin Yuan
Keyword(s):  
Angular Rate ◽  
Accuracy Improvement ◽  
Mems Gyroscope

scholarly journals System Identification of a MEMS Gyroscope

1999 ◽  
Vol 123 (2) ◽  
pp. 201-210 ◽  
Author(s):  
Robert T. M’Closkey ◽  
Steve Gibson ◽  
Jason Hui
Keyword(s):  
System Identification ◽  
Angular Rate ◽  
Experimental System ◽  
Primary Objective ◽  
Input Output ◽  
Output Data ◽  
Mems Gyroscope ◽  
Principal Axes ◽  
Frequency Split ◽  
Insight Into

This paper reports the experimental system identification of the Jet Propulsion Laboratory MEMS vibratory rate gyroscope. A primary objective is to estimate the orientation of the stiffness matrix principal axes for important sensor dynamic modes with respect to the electrode pick-offs in the sensor. An adaptive lattice filter is initially used to identify a high-order two-input/two-output transfer function describing the input/output dynamics of the sensor. A three-mode model is then developed from the identified input/output model to determine the axes’ orientation. The identified model, which is extracted from only two seconds of input/output data, also yields the frequency split between the sensor’s modes that are exploited in detecting the rotation rate. The principal axes’ orientation and frequency split give direct insight into the source of quadrature measurement error that corrupts detection of the sensor’s angular rate.

scholarly journals Magnetic Angular Rate and Gravity Sensor Based Supervised Learning for Positioning Tasks

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5364 ◽  
Author(s):  
Balázs Nagy ◽  
János Botzheim ◽  
Péter Korondi
Keyword(s):  
Noise Reduction ◽  
Supervised Learning ◽  
Sensor Fusion ◽  
Angular Rate ◽  
Translational Motion ◽  
Main Challenge ◽  
Different Types ◽  
Gravity Sensor ◽  
Position Data ◽  
Signal Processing Methods

This paper deals with sensor fusion of magnetic, angular rate and gravity sensor (MARG). The main contribution of this paper is the sensor fusion performed by supervised learning, which means parallel processing of the different kinds of measured data and estimating the position in periodic and non-periodic cases. During the learning phase, the position estimated by sensor fusion is compared with position data of a motion capture system. The main challenge is avoiding the error caused by the implicit integral calculation of MARG. There are several filter based signal processing methods for disturbance and noise estimation, which are calculated for each sensor separately. These classical methods can be used for disturbance and noise reduction and extracting hidden information from it as well. This paper examines the different types of noises and proposes a machine learning-based method for calculation of position and orientation directly from nine separate sensors. This method includes the disturbance and noise reduction in addition to sensor fusion. The proposed method was validated by experiments which provided promising results on periodic and translational motion as well.

An improved noise reduction algorithm based on wavelet transformation for MEMS gyroscope

2015 ◽  
Vol 8 (4) ◽  
pp. 413-418 ◽  
Author(s):  
Jianguo Yuan ◽  
Yantao Yuan ◽  
Feilong Liu ◽  
Yu Pang ◽  
Jinzhao Lin
Keyword(s):  
Noise Reduction ◽  
Wavelet Transformation ◽  
Reduction Algorithm ◽  
Mems Gyroscope ◽  
Noise Reduction Algorithm

Research on Testing Method of Dynamic Characteristic for MEMS-Gyroscope

2011 ◽  
Vol 346 ◽  
pp. 515-520 ◽  
Author(s):  
Nuer Zhang ◽  
Yong Feng Ren ◽  
Sheng Kun Li
Keyword(s):  
Dynamic Characteristics ◽  
Vibration Frequency ◽  
Steel Plate ◽  
Angular Rate ◽  
Test Method ◽  
Good Precision ◽  
Mems Gyroscope ◽  
Testing Method ◽  
Calibration Experiment ◽  
The Relationship

Gave a test method of the dynamic characteristics about gyroscope. In the process of using Gyroscope, it will be affected by various external factors irresistibly in making the gyroscope working at certain vibration frequency. Designed the experiments of steel plate vibrating to examine the relationship between the sensitive angular rate of gyroscope and vibration frequency. Firstly, fixed the plate on the desk; then made the plate to vibrate, collected the data from the gyroscope which vibrating with steel plate. To analyze the data of collection, gained the dynamic characteristics of gyroscope. In addition, designed rotating floor calibration experiment, calibrated the relationship between input and output of gyroscope and verified the rationality of the steel plate vibrating experiment. The result prove that gain the dynamic characteristics of gyroscope by steel plate vibrating, not only the method is simple but the result also can achieve the good precision.

Kurtosis-based IMM filter for multiple MEMS gyroscopes fusion

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 237-246 ◽  
Author(s):  
Qiang Shen ◽  
Jieyu Liu ◽  
Huang Huang ◽  
Qi Wang ◽  
Weiwei Qin
Keyword(s):  
Signal Processing ◽  
Transition Probabilities ◽  
Angular Rate ◽  
Processing Method ◽  
Multiple Model ◽  
Content Type ◽  
Mems Gyroscope ◽  
Signal Processing Method ◽  
Mems Gyroscopes ◽  
Markov Parameter

Purpose The purpose of this study is to explore a signal processing method to improve the angular rate accuracy of micro-electro-mechanical system (MEMS) gyroscope by combining numerous gyroscopes. Design/methodology/approach To improve the dynamic performance of the signal processing method, the interacting multiple model (IMM) can be applied to the fusion of gyroscope array. However, the standard IMM has constant Markov parameter, which may reduce the model switching speed. To overcome this problem, an adaptive IMM filter is developed based on the kurtosis of the gyroscope output, in which the transition probabilities are adjusted online by utilizing the dynamic information of the rate signal. Findings The experimental results indicate that the precision of the gyroscope array composed of six gyroscopes increases significantly and the kurtosis-based adaptive Markov parameter IMM filter (K-IMM) performs better than the baseline methods, especially under dynamic conditions. These experiments prove the validity of the proposed fusion method. Practical implications The proposed method can improve the accuracy of MEMS gyroscopes without breakthrough on hardware, which is necessary to extend their utility while not restricting the overwhelming advantages. Original/value A K-IMM algorithm is proposed in this paper, which is used to improve the angular rate accuracy of MEMS gyroscope by combining numerous gyroscopes.

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