Study of a Self-adaptive Kalman Filter Method in NGMIMU/GPS Integrated Navigation Scheme

In a Non-gyro micro inertial measurement unit (NGMIMU) system, an inevitable accumulation error of navigation parameters is produced due to the existence of the dynamic noise of the accelerometer output. When designing an integrated navigation system which is based on a nine-configuration NGMIMU and a single antenna Global Positioning System (GPS) by using the conventional Kalman filter (CKF), the filtering results are divergent because of the complicity of the system measurement noise. So a self-adaptive Kalman filter (SAKF) is applied in the design of NGMIMU/GPS to solve the uncertainty of the statistical characteristics of the two noises above. This filtering approach optimizes the filter by judging the prediction residuals of the filtering and calculating the statistical characteristics of the noises by using the maximum a posterior estimator. A simulation case for estimating the position, velocity and angle rate is investigated by this approach. Results verify the feasibility of the SAKF.

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