基于GPS/INS组合导航的改进自适应渐消卡尔曼滤波算法

基于GPS/SINS组合导航系统的模型不准确或者量测噪声多变所产生的滤波发散问题，研究了自适应渐消卡尔曼滤波对于滤波发散的抑制作用，文章提出一种利用新息协方差估计值和量测值实时自适应计算渐消因子的方法，用它调节卡尔曼滤波方程中预测误差协方差阵和增益矩阵，调整历史新息和当前新息的权重达到抑制滤波发散的目的。该算法能有效减少严格收敛判据推导渐消因子的计算量和限制条件，有效利用了当前新息值。仿真验证表明，提出的算法能有效抑制滤波发散，并且比常规卡尔曼滤波效果更佳。

Improved algorithm of adaptive fading Calman filtering based on GPS/INS integrated navigation

The filtering divergence problem of GPS/SINS integrated navigation system model is not accurate or measurement noise variable based on the generated, study the adaptive fading Kalman filter for inhibition of filtering divergence, this paper puts forward a kind of using the innovation covariance estimated value and the measured value calculation method of real-time adaptive fading factor, use it to control prediction error covariance matrix and gain matrix Kalman filter equation, adjusting the history of new information and the new information weight to restrain the divergence of filtering. Calculation and limitation of the algorithm can effectively reduce the strict convergence criteria are derived in a fading factor, the effective use of the new information value. The simulation results show that, the proposed algorithm can effectively restrain the divergence of Kalman filter, and the filtering effect is better than the conventional Kalman.