一种SINS/GPS紧组合导航系统的改进自适应扩展卡尔曼滤波算法

To deal with the problem that process noise covariance matrix and measurement noise covariance matrix in adaptive extended Kalman filtering algorithm cannot be estimated at the same time, a new kind of improved adaptive extended Kalman filtering algorithm was proposed. Based on residual sequence, this algorithm mainly improved the adaptive estimator of filtering algorithm, which could estimate process noise at real-time after improvement. Based on this algorithm, a new filter was designed to be applied to SINS/GPS tightly-coupled integrated navigation system, which could automatically adjust covariance matrix as noise varied in the system. Finally, extended Kalman filtering (EKF) and the improved adaptive extended Kalman filtering (AEKF) were applied respectively to simulate SINS/GPS tightly-coupled models. Tests show that the improved adaptive extended Kalman filtering has fewer positioning errors and velocity errors, and better stability of filtering than EKF.