Capacitive vs Piezoresistive MEMS Gyroscopes: A Theoretical and Experimental Noise Comparison

Giacci, Federico; Dellea, Stefano; Langfelder, Giacomo

doi:10.1016/j.proeng.2015.08.652

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…Angular velocity can be numerically integrated to obtain rotation, but MIMUs designed for human motion analysis are based on low-cost microelectromechanical technology that also suffers from substantial noise and bias over the measurement [ 5 ]. This noise and bias particularly affects the angular velocity measure, [ 6 ] such that the estimation of the orientation by numerical integration leads to a well-known error: orientation drift [ 7 ]. To improve the orientation calculation, the sensor’s data are then combined [ 8 ], the linear acceleration and the magnetic field being used to correct the initial estimate obtained by numerical integration of the angular velocity [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

Nez

Fradet

Marin

et al. 2018

Sensors

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Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by means of a Kalman filter that fuses acceleration, angular velocity, and magnetic field measures. A critical point when implementing a Kalman filter is the initialization of the covariance matrices that characterize mismodelling and input error from noisy sensors. The present study proposes a methodology to identify the initial values of these covariance matrices that optimize orientation estimation in the context of human motion analysis. The approach used was to apply motion to the sensor manually, and to compare the orientation obtained via the Kalman filter to a measurement from an optoelectronic system acting as a reference. Testing different sets of values for each parameter of the covariance matrices, and comparing each MIMU measurement with the reference measurement, enabled identification of the most effective values. Moreover, with these optimized initial covariance matrices, the orientation estimation was greatly improved. The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering. However, the methodology should be improved in order to reduce the duration of the whole process.

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Section: Introductionmentioning

confidence: 99%

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

Nez

Fradet

Marin

et al. 2018

Sensors

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“…It can be suppressed by optimizing the structural design parameters and vacuum packaging. In contrast, the circuit noise is relatively complicated [7][8][9][10] , the noise source mainly comes from the noise introduced by the circuit and each module of the system, such as RC noise, amplifier, multiplier, quantization noise of ADC / DAC, cut off of digital circuit noise and so on. In addition, the noise of the circuit is much larger than the mechanical thermal noise, which plays a major role in the total noise, therefore, this paper analyzes the noise model of the front-end readout circuit, op amp circuit and ADC / DAC circuit module in detail, in order to guide the parameter design of the circuit system, the corresponding mathematical theory is deduced and the corresponding experimental data are given.…”

Section: Introductionmentioning

confidence: 99%

Noise Model Analysis of MEMS Gyroscope Circuit Control System

Liu¹,

Chen²,

He³

2018

Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)

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The detection signal of MEMS resonant gyroscope is very weak, and it is sensitively affected by the mechanical structure and the noise of the detection circuit. Noise is an important parameter which affecting gyroscope resolution, angle random walk coefficient and zero partial stability. In this paper, the noise model is established for the front-end solitary circuit, filter circuit, ADC/DAC and other key circuit modules of the gyro control system drive closed loop and sense closed loop, through the mathematical theory, the paper deduces the noise of the key circuit module, instructs the control system parameter design, and gives the corresponding simulation results.

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Piezoresistive Nanowire-Based Electromechanical Sensors

Alaca¹,

Karimzadehkhouei²

2023

Encyclopedia of Sensors and Biosensors

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Capacitive vs Piezoresistive MEMS Gyroscopes: A Theoretical and Experimental Noise Comparison

Cited by 8 publications

References 6 publications

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

Noise Model Analysis of MEMS Gyroscope Circuit Control System

Piezoresistive Nanowire-Based Electromechanical Sensors

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