一种新的鲁棒非线性卡尔曼滤波

Huber方法是一种基于l1/l2联合范数的估计方法,该方法可以实现估计的鲁棒性,同时尽量不损失滤波精度和效率.基于Huber估计的无味卡尔曼滤波虽提高了无味卡尔曼滤波的鲁棒性,但这种方法用统计线性回归模型来近似非线性的观测模型,损失了无味变换的精度.从Huber方法的数学意义出发,对观测信息(观测值或观测噪声)进行重新构造,然后对精确的非线性观测方程进行标准的无味卡尔曼滤波,这种新的基于Huber方法的无味卡尔曼滤波无需对非线性观测方程进行线性近似,在保持鲁棒性的前提下提高了滤波精度.通过一个具有混合高斯分布观测噪声的简明实例,验证了新算法在鲁棒性、滤波精度以及估计一致性方面的优势.

New Kind of Robust Nonlinear Kalman Filter

Huber method combines l1/l2 norms and provides robustness and sufficiently good precision and efficiency.In the Huber-based unscented Kalman filter,the nonlinear measurement equations are approximated by statistical linear regressed ones,but such approximation weaks the precision of unscented transformation.So,a new kind of Huber-based unscented Kalman filter is presented.Measurement information(measurements or measurements noise) are reformulated using Huber's cost function,and the standard unscented Kalma...