针对非线性条件下机动目标跟踪问题，在此提出了一种增广交互式多模型容积卡尔曼滤波(AIMMCKF)算法。该算法将交互式多模型容积卡尔曼滤波(IMMCKF)应用到一个非线性状态增广系统，以得固定延迟平滑状态估计。同时，采用增广转换操作处理所用模型集中的不同模型可能属于不同状态空间的问题，保证算法能够正常进行。仿真结果表明，与传统非线性跟踪算法相比，所提算法在机动目标跟踪方面有更高的精度与更强的适应性。

In order to solve tracking problem of maneuvering target in nonlinear background, an Augmented Interacting Multiple Model Cubature Kalman Filter (AIMMCKF) algorithm is put forward. To obtain the fixed-lag smoothing state estimation, IMMCKF approach is applied to a nonlinear state-augmented system in the proposed algorithm. At the same time, to tackle different models problem within being represented in different state spaces, corresponding augmented conversion operation can be used. The simulation results show that, the proposed algorithm achieves higher precision and stronger adaptability for maneuvering target tracking in comparison with traditional nonlinear tracking algorithms.