Abstract
In this paper, an Adaptive Extended Kalman Filter algorithm for low-cost MEMS IMU data processing is proposed. Through the innovation calculated in each iteration of Kalman filter, the algorithm judges the motion state of the carrier, and adaptively adjusts the process noise matrix Q and the observation noise matrix R in the Kalman filter. In order to verify the effectiveness of this method in practical application, a serial of real-time experiments are conducted. The experimental results show that the adaptive adjustment of the process noise matrix and the measurement noise matrix can make the filter have better denoising ability when the carrier is nearly stationary, and better tracking ability when the carrier is moving rapidly.
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