Factory Oriented Technique for Thermal Drift Compensation in MEMS Capacitive Accelerometers

Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve their performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated using a lightweight algorithm by knowing the characteristic thermal parameters of the accelerometer (Temperature Drift of Bias and Temperature Drift of Scale Factor). These parameters vary in each accelerometer and axis, making an individual calibration necessary. In this work, a simple and fast calibration method that allows the characteristic parameters of the three axes to be obtained simultaneously through a single test is proposed. This method is based on the study of two specific orientations, each at two temperatures. By means of the suitable selection of the orientations and the temperature points, the data obtained can be extrapolated to the entire working range of the accelerometer. Only a mechanical anchor and a heat source are required to perform the calibration. This technique can be scaled to calibrate multiple accelerometers simultaneously. A lightweight algorithm is used to analyze the test data and obtain the compensation parameters. This algorithm stores only the most relevant data, reducing memory and computing power requirements. This allows it to be run in real time on a low-cost microcontroller during testing to obtain compensation parameters immediately. This method is aimed at mass factory calibration, where individual calibration with traditional methods may not be an adequate option. The proposed method has been compared with a traditional calibration using a six-sided orthogonal die and a thermal camera. The average difference between the compensations according to both techniques is 0.32 mg/°C, calculated on an acceleration of 1 G; the maximum deviation being 0.6 mg/°C.

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High-accuracy current measurement with low-cost shunts by means of dynamic error correction

Journal of Sensors and Sensor Systems ◽

10.5194/jsss-5-389-2016 ◽

2016 ◽

Vol 5 (2) ◽

pp. 389-400 ◽

Cited By ~ 6

Author(s):

Patrick Weßkamp ◽

Joachim Melbert

Keyword(s):

Measurement Error ◽

Error Correction ◽

Low Cost ◽

Dynamic Error ◽

Real Life ◽

Lithium Ion ◽

Calibration Method ◽

Correction Algorithm ◽

Temperature Drift

Abstract. Measurement of electrical current is often performed by using shunt resistors. Thermal effects due to self-heating and ambient temperature variation limit the achievable accuracy, especially if low-cost shunt resistors with increased temperature coefficients are utilized. In this work, a compensation method is presented which takes static and dynamic temperature drift effects into account and provides a significant reduction of measurement error. A thermal model of the shunt resistor setup is derived for this purpose and a suitable calibration method is developed. The correction algorithm is based upon a digital filter bank and is optimized for microcontrollers with low computational complexity. It is implemented in laboratory test equipment for long-term studies on automotive lithium-ion cells. For a 600 A current pulse, it reduces the measurement error from 2 % to less than 0.1 %. Measurements with a real-life testing profile show a reduction of remaining measurement error by 60 %. Statistical results for 100 test systems and long-term drift measurements prove the reliability of the method. The proposed dynamic error correction algorithm therefore allows high measurement accuracy despite the use of low-cost shunt resistors.

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Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network with Hadamard Transform and Additive Layers

IEEE Sensors Journal ◽

10.1109/jsen.2021.3084220 ◽

2021 ◽

pp. 1-1

Author(s):

Diaa Badawi ◽

Agamyrat Agambayev ◽

Sule Ozev ◽

A. Enis Cetin

Keyword(s):

Neural Network ◽

Real Time ◽

Chemical Sensors ◽

Deep Neural Network ◽

Low Cost ◽

Hadamard Transform ◽

Drift Compensation

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Calibration method to address influences of temperature and electrical conductivity for a low-cost soil water content sensor in the agricultural field

Agricultural Water Management ◽

10.1016/j.agwat.2021.107015 ◽

2021 ◽

Vol 255 ◽

pp. 107015

Author(s):

Yuhi Satoh ◽

Hideki Kakiuchi

Keyword(s):

Electrical Conductivity ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Low Cost ◽

Calibration Method ◽

Agricultural Field

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A Monte Carlo (MC) based individual calibration method forin vivox-ray fluorescence analysis (XRF)

Physics in Medicine and Biology ◽

10.1088/0031-9155/52/7/015 ◽

2007 ◽

Vol 52 (7) ◽

pp. 2009-2019 ◽

Cited By ~ 8

Author(s):

Marie Hansson ◽

Mats Isaksson

Keyword(s):

Monte Carlo ◽

Fluorescence Analysis ◽

Calibration Method ◽

Individual Calibration

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A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

International Journal of Navigation and Observation ◽

10.1155/2015/503814 ◽

2015 ◽

Vol 2015 ◽

pp. 1-18 ◽

Cited By ~ 11

Author(s):

Heikki Hyyti ◽

Arto Visala

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Low Cost ◽

Direction Cosine ◽

Estimation Algorithm ◽

Calibration Method ◽

Attitude Estimation ◽

Mems Sensors ◽

Acceleration Sensors ◽

Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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Dual mass MEMS gyroscope temperature drift compensation based on TFPF-MEA-BP algorithm

Sensor Review ◽

10.1108/sr-09-2020-0205 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Huiliang Cao ◽

Rang Cui ◽

Wei Liu ◽

Tiancheng Ma ◽

Zekai Zhang ◽

...

Keyword(s):

Training Data ◽

Experimental Result ◽

Intrinsic Mode Functions ◽

Temperature Drift ◽

Connection Weight ◽

Bp Network ◽

Content Type ◽

Mems Gyroscope ◽

Time Frequency ◽

Drift Compensation

Purpose To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network. Design/methodology/approach First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model. Findings The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10–3°/h and the angular velocity random walk value is 2.132 × 10–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10–2°/h/vHz of the original output signal of the gyro. Originality/value This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

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Gyro Drift Calibration in Low Cost Inertial System

Volume 7B: 17th Biennial Conference on Mechanical Vibration and Noise ◽

10.1115/detc99/vib-8166 ◽

1999 ◽

Author(s):

Zhong Zhao ◽

Rong Ma ◽

Weiguo Zhang

Keyword(s):

Low Cost ◽

Fuzzy Reasoning ◽

Calibration Method ◽

Inertial System ◽

Time Varying ◽

Dynamic Estimation ◽

Gyro Drift

Abstract An intelligent gyro drift calibration method for low-cost inertial system is presented in this paper. This method based on fuzzy reasoning and dynamic estimation can calibrate time-varying gyro drift in the motion of vehicle. Experiments have been done on three strapdown inertial all-attitude systems constituted of piezoelectric rate gyros. The result shows that this method is effective by which the residual of piezoelectric gyro drift can be reduced to about one percent of its original drift value.

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A Fast Calibration and Compensation Method for Magnetometers in Strap-Down Spinning Projectiles

Sensors ◽

10.3390/s18124157 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4157 ◽

Cited By ~ 4

Author(s):

Dafeng Long ◽

Xiaoming Zhang ◽

Xiaohui Wei ◽

Zhongliang Luo ◽

Jianzhong Cao

Keyword(s):

Low Cost ◽

Experimental System ◽

Calibration Method ◽

Attitude Estimation ◽

Attitude Measurement ◽

Angle Error ◽

Estimation Errors ◽

Zero Bias ◽

Launch Site ◽

Spinning Projectile

Attitude measurement is an essential technology in projectile trajectory correction. Magnetometers have been used for projectile attitude measurement systems as they are small in size, lightweight, and low cost. However, magnetometers are seriously disturbed by the artillery magnetic field during launch. Moreover, the error parameters of the magnetometers, which are calibrated in advance, usually change after extended storage. The changed parameters have negative effects on attitude estimation of the projectile. To improve the accuracy of attitude estimation, the magnetometers should be calibrated again before launch or during flight. This paper presents a fast calibration method specific for a spinning projectile. At the launch site, the tri-axial magnetometer is calibrated, the parameters of magnetometer are quickly obtained by optimal ellipsoid fitting based on a least squares criterion. Then, the calibration parameters are used to compensate for magnetometer outputs during flight. The numerical simulation results show that the proposed calibration method can effectively determine zero bias, scale factors, and alignment angle errors. Finally, a semi-physical experimental system was designed to further verify the performance of the calibration method. The results show that pitch angle error reduces from 3.52° to 0.58° after calibration. The roll angle error is reduced from 2.59° to 0.65°. Simulations and experimental results indicate that the accuracy of magnetometer in strap-down spinning projectile has been greatly enhanced, and the attitude estimation errors are reduced after calibration.

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