Humidity Error Compensating of MEMS Gyros Based on A daptive Threshold Denoising and RBF Neural Network

The large random errors in a typical MEMS gyros under the ranges of relative humidity condition. In order to solve the large random error under the ranges of relative humidity, a method based on adaptive threshold denoising and neural network is proposed in this paper. Full relative humidity experiment is carried to test LSM6DS3 MEMS gyros. Then the adaptive threshold denoising is used to separate the drift error and drift data modeled with RBF neural network. The experimental results show the effectiveness of the method, and it is of great significance in improving the performance of MEMS gyros.