The Application of Self-adaptive Kalman filter in NGIMU/GPS Integrated Navigation System

The non-gyro inertial measurement unit (NGIMU) uses only accelerometers replacing gyroscopes to compute the motion of a moving body. In a NGIMU 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 NGIMU 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 NGIMU/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 and the correctness of the SAKF