Interacting multiple model (IMM) filter is a classical method to track targets in hybrid situations. However, it can exhibit divergence when the models are correlated or the system suffers from uncertainties. The generalized covariance intersection method based on the weighted Kullback–Leibler (K–L) divergence can solve the divergence problem of correlated estimates. A novel interacting multiple model (NIMM) filter is presented that combines two different algorithms, the adaptive fading Kalman filter and the maximum correntropy Kalman filter, based on the model interacting with the weighted K-L divergence to address the uncertainty problems of the system. The NIMM filter algorithm is designed and the stability and accuracy are analyzed. The simulation results demonstrate that the proposed filter can effectively improve the accuracy under different uncertainty conditions for classical examples and ballistic trajectory tracking scenarios.

交互多模型（IMM）过滤器是在混合情况下跟踪目标的经典方法。但是，当模型相关或系统存在不确定性时，它可能表现出差异。基于加权Kullback-Leibler（KL）散度的广义协方差交集方法可以解决相关估计的散度问题。提出了一种新颖的交互多模型（NIMM）滤波器，该模型结合了模型与加权KL散度相互作用，解决了系统的不确定性问题，该模型结合了两种不同的算法：自适应衰落卡尔曼滤波器和最大熵卡尔曼滤波器。设计了NIMM滤波器算法，并分析了稳定性和准确性。