Micro electro mechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption.The\nperformances of Micro Inertial measurement unit (IMU), which is composed of MEMS inertial sensors, degrade, and error, will\nbecome larger in high dynamic environment. In order to solve the problem, a novel combined calibrationmethod for compensating\nthe deterministic error of MEMS sensors is proposed. Considering the rotation of different sensitive axes in high dynamic and low\ndynamic environment, the compounded calibration based on fuzzy neural network (FNN) is adopted to identify the coupling\ncoefficients to eliminate the adverse coupling effects between different rotation axes. Furthermore, the self-developedMicro IMU\nand magnetometer are applied in attitude estimation system. Considering the large attitude error occurred in most cases, the\napproach utilizing the estimation of error quaternion vector could avoid the calculation error due to inaccurate modeling in the\nskew symmetric matrix that comprises attitude error vector components.The intelligent Kalman filter (IKF) based on complexity\nstate equation of error quaternion is designed to improve the performance by adjusting the parameters of filter on line. The\nexperimental results show that the proposed approach could have a higher level of stability and accuracy in comparison to other\nattitude estimation algorithms.
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