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

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.