Application of an Adaptive Sequential Kalman Filter to SINS/GPS Navigation Data Fusion

For SINS/GPS integrated navigation system with unknown measurement noise covariance matrix, adopting the conventional Kalman filtering approach to estimate the navigation system errors will lead to a large state estimation error or even make the filter diverge. To solve this problem, an adaptive sequential Kalman filter is presented, in which the measurement noise covariance matrix is estimated on-line by an innovation-based adaptive estimation (IAE) method. Properly designed discontinuous feedback control law and serial measurement processing make the adaptive filter more suitable for real time implementation. Simulation results reveal that without an exact measurement noise covariance matrix, the adaptive sequential Kalman filtering approach can still estimate the errors of SINS/GPS integrated navigation system effectively.