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Inertial Positioning How to Solve the Gyro Drift and Magnetic-field Interference-2

A filter algorithm is extended on the basis of complementary filtering, such as the classical complementary filter, the gradient descent based complementary filtering algorithm and other [3-6], but the filtering algorithm is not suitable for high precision. In the face of these problems, this paper proposes an inertial positioning algorithm based on Kalman filtering and complementary filtering. In the design of Kalman filtering, the four elements obtained by the accelerometer and magnetometer are used as the observation values, and the four elements obtained by the gyroscope are used as the state values, and the filtering is accomplished through the fusion of data. For the first optimal estimation of the four element number, the gyro drift is compensated by the complementary filter designed for the gyro drift problem, and the corrected angular velocity is obtained, and then the four elements which are constantly updated after correction are obtained, and then the optimal estimated four yuan number completed for the first time is estimated by the second Kalman filtering. Output high precision attitude angle.

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Inertial positioning how to solve the gyro drift and magnetic-field interference-1
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