Design of Optimal Estimation Algorithm for Multi-Sensor Fusion of a Redundant MEMS Gyro System

Data fusion of redundant MEMS inertial sensors has become a new method for reducing sensor drift error and enhancing the accuracy of navigation systems. In this paper, a redundant MEMS gyroscope system having an optimal configuration structure is constructed. Firstly, the evaluation index of a redundant gyroscope system configuration is established, and the influencing factors of the configuration are analyzed under the condition of noise correlation in the component gyroscopes. Then redundant 4,5,6-gyro systems are designed. Secondly, the optimal KF algorithm for multi-signal fusion of the redundant gyroscope system is designed to estimate the three-dimensional orthogonal angular rate. Finally, a redundant 4-IMU system is designed. The simulation results showed that the input rate signal in the body coordinate
${X}_{b} {Y}_{b}{Z}_{b}$
can be accurately estimated and the drift error in single gyroscope can be remarkably reduced by fusing the redundant measurements. The experimental results illustrated that the ARW and RRW noise on the
${X}_{b}$
,
${Y}_{b}$
and
${Z}_{b}$
axes were reduced by a factor about 3.2 and 3.7 compared to the single gyroscope. In the swing test, the
${X}_{b}$
and
${Y}_{b}$
axes’
$1\sigma $
estimated error was reduced by a factor about 4.7 using the optimal KF algorithm compared to the single gyroscope, while on the
${Z}_{b}$
axis there was a decrease by a factor about 2.6.