Data Fusion Algorithm for Redundant Gyroscope System Based on Differential Layout Array

To overcome the phenomenon of large random noise and low precision of microelectromechanical system (MEMS) gyroscope, this paper proposes a MEMS gyroscope array with a differential layout structure. The random noise of component gyroscope is analyzed using Allan variance method, then a data fusion algorithm is designed to reduce the uncertainty of output rate signals based on differential processing and Kalman filtering. Simulation and experimental results illustrate that the method proposed in this paper can effectively reduce the impact of random noise on the accuracy of true angular rate estimation. In the static experiment, angular random walk (ARW) and rate random walk (RRW) decreased by 74.7 % and 67.1 %, respectively. Meanwhile, the real-time response can also be obtained in dynamic tests, and the standard deviation of fused rate signal has decreased from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.2114^{\circ} / s$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.0105^{\circ} / s$</tex>, which demonstrates the feasibility of the algorithm in time-varying environment.