针对平滑变结构滤波算法存在抖振以及无法有效估计未量测目标状态的问题，提出了基于交互多模型的时变平滑变结构滤波算法。该算法首先通过平滑变结构滤波算法对目标状态进行初步估计；其次通过计算时变平滑有界层，并采用tanh函数取代饱和函数计算初步状态增益，共同解决抖振问题；然后采用贝叶斯思想重新计算协方差矩阵与状态增益用于目标状态更新，解决平滑变结构滤波无法有效估计未量测状态的问题；最后与交互多模型算法结合，实现对机动目标的有效跟踪。仿真结果表明，提出的算法在模型失配、量测噪声改变以及非高斯量测噪声的情况下，仍可有效地对机动目标进行跟踪，与典型的目标跟踪方法相比，跟踪精度明显提高且鲁棒性更强。

To overcome the problems of jitter and ineffective estimation of the unmeasured target state in the smooth variable structure filter， a time-varying smooth variable structure filter based on interactive multi-model is proposed. Firstly， the target state is estimated through the smooth variable structure filter. Subsequently， the jitter problem is solved by computing the time-varying smooth boundary layer and employing the tanh function instead of the saturation function to calculate the initial state gain. Then， Bayesian formulas are applied to recompute the covariance matrix and state gain to update the target state， effectively addressing the challenge of inefficient estimation of unmeasured states in the smooth variable structure filter. Finally， the algorithm is integrated with the interactive multi-model approach to achieve effective tracking of maneuvering targets. Simulation results demonstrate that the proposed algorithm maintains its effectiveness in tracking maneuvering targets under the conditions of model mismatch and changes in measurement noise or non-Gaussian measurement noise. The simulation results indicate that the method proposed has a significant improvement in tracking accuracy and robustness over typical target tracking methods.