A Low cost inverse-of-matrix-free Kalman Filter for Combining Multiple MEMS Gyroscopes

Due to the mass, power and computational constraint of nano-satellite, high performance gyroscope is typically not available. In our previous nano-satellite mission named STU-2 which was launched in September 2015, the mems gyro was used, but its performance is highly susceptible. Due to the micro-electromechanical systems (MEMS) based gyroscope noise. This paper presents a low complexity Kalman filter based gyro drift filtering approach which utilizes the present states of the multiple MEMS gyroscope. An inverse-of-matrix-free KF was briefly presented to obtain an optimal estimate of input rate signal, which reduce the computational complexity and meet the need of reducing noise disturbance. Finally, an integrated system consisting of a six-gyroscope was built up and tested. The experimental results show that the capable of reducing the gyroscope noise in all axes.