Low-Orbit Augmented Positioning Technology Based on Adaptive Iterative Extended Kalman Filter

The BeiDou-3 satellite navigation system (BDS-3) provides all-weather, all-time, high-precision positioning, navigation, and timing services for global users. However, compared with the Asia-Pacific region, the BDS-3 suffers from a relatively small number of visible satellites at high latitudes, which makes it difficult to provide high-performance services that meet application requirements. Moreover, traditional satellite positioning algorithms, such as iterative least squares (ILS) and extended Kalman filter (EKF), yield unsatisfactory positioning results when con-fronted with nonlinear models and abnormal noise. The rapid movement of low Earth orbit (LEO) satellites can enhance geometric diversity and effectively improve the geometry of BDS-3. The adaptive iterative extended Kalman filter (AIEKF) can adaptively adjust the noise covariance matrix and iteratively optimize the filtered estimates to converge more closely towards the true values. Therefore, we leverage the Iridium NEXT constellation to enhance the performance of BDS-3, and based on this, employ the AIEKF to estimate the system state parameters. The results indicated that with the integration of the LEO constellations, the number of visible satellites of BDS-3 at high latitudes improved by about 3 satellites and the position dilution of precision decreased by about 0.1821. The positioning accuracy was improved by 54.81% and 23.30% through the proposed algorithm, when compared with the ILS and EKF respectively. The algorithm provides a reference for comprehensive assessment of hybrid constellation positioning performance.