一种鲁棒的Kalman滤波方法

A Robust Kalman Filter Method

在目标跟踪测量中,由于各种噪声的影响,Kalman滤波算法对野值数据的估计经常出现较大误差,直接影响了定位结果。修正增益的滤波方法在野值相对较小的情况下误差不能有效的识别。为此,提出残差权重自适应抗野值的Kalman滤波方法,该方法能明显的降低野值对定位结果的影响,对实测数据的仿真结果表明了该方法的有效性,满足试验要求。

Since affected by kinds of noise in target tracking,the evaluation of measurement outliers using the normal Kalman filter algorithm often deviate seriously and hence the positioning result of target is interfered.The Kalman filter algorithm with gain revised does not effectively identify the outlier relatively less than normal.Based on the situation mentioned,a robust Kalman filter algorithm is proposed in this paper,which can obviously reduce the influence of outlier error.The simulation of practical data confirms that the validity of this method meets the requirement of engineering practice.