Drift Error Calibration Method Based on Multi-MEMS Gyroscope Data Fusion

This article is concerned with the method of calibrating the temperature drift and random drift of Micro-Electro-Mechanical System (MEMS) gyroscope to improve the precision of MEMS gyroscope application. These methods include two steps. In the first step, the optimized BP neural network is used to establish the temperature drift error model of the MEMS gyroscope, and the B-spline method is used to improve the storage and calculation efficiency of the temperature drift calibration. In the second step, since the random drift error of a single sensor is difficult to reduce the data of multiple MEMS gyroscope sensors that have completed temperature drift calibration are fused.The error calibration evaluation indicators all use SSE (The sum of squares due to error) to compare the advantages of the proposed method.In the experimental part,the four most common MEMS chips MPU6050(including a three-axis gyroscope and accelerometer sensor) are used in the market to verify the effect of the proposed method in drift error calibration. Finally, it is proved that the data fusion of a Multi-sensor is not only significantly better than the random drift error of a single sensor. Moreover, with the increase in the number of fusion sensors, the calibration effect has a trend of gradual improvement.