SINS/BDS紧组合系统中自适应 两阶段扩展卡尔曼滤波

在实际应用中，以伪距/伪距率为观测量的SINS/BDS紧组合导航系统，存在量测噪声的统计特性与实际不相符的情况，传统扩展卡尔曼滤波（EKF）方法无法有效解决这一问题，从而引起滤波误差增大。提出了一种SINS/BDS紧组合导航系统的GDOP估算及在线估计量测噪声的自适应两阶段EKF（ATEKF）方法，该方法使用经过紧组合修正后的SINS输出的位置，并结合星历数据中提供的卫星位置求解GDOP。在此基础上，利用GDOP值以及新息，实现了紧组合导航系统的量测噪声方差阵（Rk）的在线实时估计，从而达到自适应滤波的效果，改善导航精度。

Adaptive Two-Stage Extended Kalman Filter Algorithm for SINS/BDS Tightly Integrated System

In practical application, the SINS/BDS tightly integrated navigation system with pseudo range/pseudo range rate as the observation measurement is faced with the situation that measurement noise statistical characteristics are inconsistent with reality. However, traditional extended Kalman filter(EKF) method can''t effectively solve it, causing large filter errors in this system. This paper presents an adaptive two-stage extended Kalman filter(ATEKF) method of GDOP estimation and online estimation of measurement noise for SINS/BDS tight integrated navigation system, which combines satellite positions of ephemeris with corrected SINS position instead of BDS positioning results to calculate GDOP. On this basis, using the GDOP value and innovation, the measurement noise variance matrix Rk online real-time estimation is realized to achieve the adaptive filter effect and improve navigation accuracy in tightly integrated navigation system.