Study on SINS/GPS tightly integrated navigation based on Adaptive Extended Kalman Filter

This paper presents a novel SINS/GPS tightly integrated navigation algorithm based on Adaptive Extended Kalman Filtering. This algorithm is mainly used in vehicle SINS/GPS integrated navigation system to deal with time varied noise statistic in different working conditions. First, measurement noise covariance is estimated through innovation sequence online, then the covariance matching algorithm is used to track the process noise real-time based on the system equation. More, scale factor is introduced to reduce truncation error caused by Taylor formulation and thus improve estimation accuracy. The Simulations results show that, compared with the traditional extended kalman filter algorithm and unscented kalman filter algorithm, the proposed algorithm is able to estimate the changes of both process and observation noise statistics simultaneous, and have higher precision and more robustness.