Two-Axis Accelerometer Calibration and Nonlinear Correction Using Neural Networks: Design, Optimization, and Experimental Evaluation

Currently, there is no robust method that could calibrate the accelerometer output without explicitly deriving the error model of the device and estimate the nonlinear parameters of the model. This article presents a methodology to approximate the output of two-axis thermal accelerometers based on n...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2020-09, Vol.69 (9), p.6787-6794
Main Authors: Soriano, Mario A., Khan, Faheem, Ahmad, Rafiq
Format: Article
Language:English
Subjects:
Acceleration
Accelerometer
Accelerometers
Calibration
Cost function
Data points
Design optimization
Estimation
Mathematical model
Neural networks
neural networks (NNs)
nonlinearities
optimization
Parameter estimation
Sensors
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Summary:Currently, there is no robust method that could calibrate the accelerometer output without explicitly deriving the error model of the device and estimate the nonlinear parameters of the model. This article presents a methodology to approximate the output of two-axis thermal accelerometers based on neural networks (NNs) for calibration and nonlinear correction. This method uses the output of the accelerometer and the Earth's gravitational acceleration expected at a static position as data for training. The proposed method uses different optimization methods (adaptive moment estimation (ADAM), gradient descent, and gradient descent with momentum) to find the best solution using half mean squared error (HMSE) as the cost functions for evaluation. Experiments are conducted and presented to validate the NN-based calibration method using 2800 unseen data points.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.2978568