Temperature drift compensation for inclinometer sensors based on Kalman filtering and neural networks

Binxin Hu; Xiang Xu; Pengcheng Hao; Rong Zhang; Feng Zhu

doi:10.1117/12.3025592

28 February 2024 Temperature drift compensation for inclinometer sensors based on Kalman filtering and neural networks

Binxin Hu, Xiang Xu, Pengcheng Hao, Rong Zhang, Feng Zhu

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 1307140 (2024) https://doi.org/10.1117/12.3025592
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

In embedded systems, precision MEMS inclinometers often experience temperature drift due to environmental changes during operation. This paper introduces Kalman filtering as a preprocessing step and combines it with neural network-based temperature compensation methods. Experimental verification shows that within the range of 0-50 degrees Celsius, the signal quality retention rate is 97.2%, the signal-to-noise ratio reaches 21.68 dB, and the temperature drift phenomenon is reduced by 85.96%, ensuring the effectiveness and feasibility of this method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Binxin Hu, Xiang Xu, Pengcheng Hao, Rong Zhang, and Feng Zhu "Temperature drift compensation for inclinometer sensors based on Kalman filtering and neural networks", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 1307140 (28 February 2024); https://doi.org/10.1117/12.3025592

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available