A Low Complexity Kalman Filter for Improving MEMS based Gyroscope Performance

Abstract

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 VELOX-I which was launched in June 2014, the sun tracking algorithm used an observer free quaternion error correction algorithm, but its performance is highly susceptible to the microelectromechanical systems (MEMS) based gyroscope noise. This paper presents a low complexity Kalman filter (LCKF) based gyro drift filtering approach which utilizes the present states of the MEMS gyroscope. Low complexity was achieved by expressing the state transition matrix and the observation matrix into sparse matrices with non-zero diagonal elements. The performance of the proposed approach has been evaluated experimentally using the hardware of VELOX-I, a two axes rotary table and a sun simulator. Besides a 40.81% reduction in computational time, the experimental results show that the LCKF is capable of reducing the gyroscope noise in all axes. Overall, the experimental results agreed well with the simulation results and it has validated the improvement in the sun tracking performance.