Research on temperature drift compensation method of mems gyroscope based on neural network

Aiming at the problem that MEMS inertial sensors are very sensitive to temperature changes, a real-time temperature compensation method for MEMS gyroscopes is proposed, and the compensation effect of this method is compared with the traditional piecewise linear fitting method. First, an in-house-designed MEMS-IMU for temperature effect tests, and the output of the MEMS gyroscope is collected during the test; Second, a real-time compensation model based on gyroscope output is proposed at the last moment, and the false nearest neighbor algorithm is introduced to determine the number of neurons in the hidden layer of the neural network. Then, a variant of the standard recurrent neural network, which is the gated recurrent neural network, is used to identify the relationship between the input and output of the proposed model, and a complete real-time compensation model for the temperature drift of the MEMS gyroscope is obtained. Finally, We compare the proposed and the piecewise linear fitting methods’ compensation effects. Toconditions and different heating rate conditions, respectively. According to the comparison results, under the above two test conditions, the Allen variance coefficient and zero bias stability of the MEMS gyroscope are significantly reduced after the compensation by the proposed method, and the compensation effect is better than the segmented linear fitting method. Therefore, the method proposed in this paper effectively compensates for the temperature drift of the MEMS gyroscope.