多渐消因子Kalman滤波器在SINS初始对准中的应用(英文)

针对系统模型和统计信息不能精确已知的条件下Kalman滤波无法给出最优解这一问题,单一渐消因子Kalman滤波算法对于简单的系统是有效的,但是对于复杂的多变量系统,仅仅利用单个的渐消因子是不够的。本文提出了一种多渐消因子滤波算法,通过利用开窗法计算新息序列协方差的无偏估计获得渐消因子矩阵。利用渐消因子矩阵调节一步预测均方误差矩阵k|k1P,对不同的滤波通道提供不同的渐消速率。将该方法应用于SINS的初始对准中,仿真和试验结果表明:当真实系统噪声统计特性同设定参数不一致时,对准精度明显高于其他滤波算法。其对不确定性噪声具有较低的敏感度,对系统参数具有较好的滤波效果。因而,在实际应用中具有重要的参考价值。

Multiple Fading Factors Kalman Filter for SINS Static Alignment Application

To solve the problem that the standard Kalman filter cannot give the optimal solution when the system model and stochastic information are unknown accurately, single fading factor Kalman filter is suitable for simple systems. But for complex systems with multi-variable, it may not be sufficient to use single fading factor as a multiplier for the covariance matrices. In this paper, a new multiple fading factors Kalman filtering algorithm is presented. By calculating the unbiased estimate of the innovation sequence covariance using fenestration, the fading factor matrix is obtained. Adjusting the covariance matrix of prediction error P_k|k-1 using fading factor matrix, the algorithm provides different rates of fading for different filter channels. The proposed algorithm is applied to strapdown inertial navigation system (SINS) initial alignment, and simulation and experimental results demonstrate that, the alignment accuracy can be upgraded dramatically when the actual system noise characteristics are different from the pre-set values. The new algorithm is less sensitive to uncertainty noise and has better estimation effect of the parameters. Therefore, it is of significant value in practical applications.