针对复杂环境下自主导航系统抗拒止、强鲁棒等应用需求，提出了一种基于交互多模型的鲁棒滤波方法(IMM-RCKF)。该方法通过量测不确定性量化与非线性滤波器估计，利用量测中断前滤波新息，结合先验知识调整后验概率矩阵权值，优化鲁棒容积卡尔曼滤波器的参数误差模型，选用动静态滤波的方法对两者做交互融合处理，形成了高阶非线性多模型滤波器，增强了滤波器对复杂环境的自适应性和干扰环境的鲁棒性。将其应用到某空天飞行器的自主导航系统中，结果表明，基于IMM原理融合鲁棒滤波的多模型滤波方法，有效提高了导航系统的收敛速度与估计精度，相比于鲁棒卡尔曼滤波方法，位置精度提高约14.3%，速度精度提高约11.2%，并进一步提高了系统抗干扰能力，有效解决了传统卡尔曼滤波器难以处理模型误差的难题。
To address the application requirements of anti-jamming and enhanced robustness for autonomous navigation systems in com-plex environments, this paper proposes a robust filtering method based on the Interactive Multiple Model (IMM) framework. The method integrates measurement uncertainty quantification and nonlinear filter estimation, leveraging the filter innovation before measurement interruption to dynamically adjust the posterior probability matrix weights through prior knowledge. By optimizing the parametric error model of the Robust Cubature Kalman Filter (RCKF) and adopting a hybrid static-dynamic fil-tering strategy for interactive fusion, a high-order nonlinear multi-model filter is developed. This architecture significantly en-hances the environmental adaptability and interference robustness of the filter. The proposed method is implemented in the au-tonomous navigation system of a hypersonic aerospace vehicle. Experimental results demonstrate that the IMM-based multi-model filtering approach, which fuses robust filtering principles, effectively improves the convergence speed and estimation accuracy of the navigation system. Compared with conventional robust Kalman filtering methods, the position accuracy is in-creased by approximately 14.3% and the velocity accuracy by 11.2%. Furthermore, the system exhibits superior anti-interference capability, successfully addressing the long-standing challenge of traditional Kalman filters in handling model errors under dy-namic uncertainties.
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