As an important inertial sensor, the gyroscope is mainly used to measure angular velocity in inertial space. However, due to the influence of semiconductor thermal noise and electromagnetic interference, the output of the gyroscope has a certain random noise and drift, which affects the accuracy of the detected angular velocity signal, thus interfering with the accuracy of the stability of the whole system. In order to reduce the noise and compensate for the drift of the MEMS (Micro Electromechanical System) gyroscope during usage, this paper proposes a Kalman filtering method based on information fusion, which uses the MEMS gyroscope and line accelerometer signals to implement the filtering function under the Kalman algorithm. The experimental results show that compared with the commonly used filtering methods, this method allows significant reduction of the noise of the gyroscope signal and accurate estimation of the drift of the gyroscope signal, and thus improves the control performance of the system and the stability accuracy.

Photoelectric stabilization platform is an optoelectronic imaging tracking system widely used in aircraft, vehicles and other equipment. It can effectively isolate the disturbance of the carrier and ensure the stability of the platform. In practical applications, there are many factors that affect the stability of the platform, including friction, disturbance, and mass imbalance. The purpose of this paper is to use Simulink to model and simulate the integrated three-axis photoelectric stability platform, and to explore the influence of mass imbalance torque caused by different factors on the accuracy and stability of the photoelectric stability platform. The results show that the unbalanced moment generated by the eccentricity of the frame has a great influence on the accuracy of the platform, and the unbalanced torque generated by the uneven mass distribution has little or no negligible influence on the accuracy of the platform.