基于自适应多重渐消因子卡尔曼滤波的SINS初始对准方法

SINS initial alignment method based on adaptive multiple fading factors Kalman filter

针对传统卡尔曼滤波器在模型失配和噪声时变情况下滤波精度下降甚至发散的问题,设计了一种新的多重渐消因子卡尔曼滤波算法。该算法通过一个基于渐消记忆指数加权的新息协方差估计器来计算新息协方差估计值,并依此引入多重渐消因子对预测误差协方差阵进行调整,使得各滤波通道具有不同的调节能力,克服了单渐消因子对多变量跟踪能力差的局限性,从而提高滤波算法的精度和鲁棒性。仿真和试验结果表明,新算法能有效抑制滤波器发散,其滤波精度和鲁棒性优于常规卡尔曼滤波与单渐消因子卡尔曼滤波,能够更好地满足工程应用的要求。

In view of poor accuracy and divergent of the traditional Kalman filter （KF） algorithm when there exits model errors and noise, a new multiple fading factors KF is proposed, which is based on the innovation covariance estimator of the fading memory index weighting to calculate the innovation covariance estimator. The new algorithm can adjust prediction error covariance matrix by multiple fading factors, so that each filter channel has different regulatory capacity, which improves the accuracy and robustness of the filter algorithm even if there exits poor tracking between the single fading factor and multivariate. The simulation and experiment results indicate that the new algorithm can effectively suppress the filter divergence, compared with the conventional KF and single fading factor KF, and the filter accuracy and robustness are improved, which can better meet the requirements of engineering applications.