关键词: 非线性滤波, 容积卡尔曼滤波（CKF）, Huber方法, 鲁棒性, 固定点算法

Abstract:

For a nonlinear system, the Cubature Kalman Filter（CKF） is a useful method to deal with the state estimation problems and can obtain good performance under Gaussian noise. However, its performance often degrades dramatically when the noises are contaminated by some heavy-tailed noises. To cope with this problem, the Huber methodology is applied to the framework of CKF to improve its robustness instead of the traditional Minimum Mean Square Error（MMSE） criterion. In the proposed filter, a linear regression model is formulated by linearizing the measurement equation, and the fixed-point algorithm is used to solve the Huber-based minimization problem. Hence the Fixed-Point Iterated Huber-based robust Cubature Kalman Filter（FP-IHCKF） is derived, in which the prior information and measurement information are reformulated using the Huber methodology. The effectiveness and robustness of the proposed filter are demonstrated through numerical simulation study on a re-entry target tracking problem.

Key words: nonlinear filter, Cubature Kalman Filter（CKF）, Huber methodology, robustness, fixed-point algorithm