基于改进强跟踪ASCKF算法的SINS/GNSS组合对准方法

大方位失准角下的SINS/GNSS组合对准系统呈非线性,采用传统的卡尔曼滤波方法进行初始对准易导致对准精度下降甚至滤波发散。基于此,提出了一种基于改进强跟踪自适应平方根容积卡尔曼滤波算法的组合对准方法。该方法采用QR分解求取协方差的分解因子,并在状态预测方差阵的平方根更新中引入多重渐消因子调整滤波增益;同时,基于Sage-Husa自适应滤波,引入改进的时变噪声估计器实时估计噪声的统计特性。仿真结果表明,采用改进的滤波算法进行大方位失准角下的组合对准,对准精度明显提高。

SINS/GNSS Integrated Alignment Method Based on Improved Strong Tracking ASCKF Algorithm

As the SINS/GNSS integrated alignment system with a large azimuth misalignment ang-le is non-linear and the traditional Kalman filter method for initial alignment would lead to poor alignment accuracy or even filtering divergence, a SINS/GNSS integrated alignment method based on the improved strong tracking Adaptive Square-root Cubature Kalman Filter (ASCKF) algori-thm is proposed in this paper. The proposed method directly adopts QR factorization to get the factor of covariance matrix and introduces the multiple fading factors to adjust the filtering gain during the square root updating of state prediction covariance matrix. Combined with Sage-Husa adaptive filter, an improved noise statistics estimator is designed to estimate noise statistics in real time. Simulation results show that the proposed algorithm can increase the accuracy of integrated alignment with a large azimuth misalignment angle.