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Acta Aeronautica et Astronautica Sinica ›› 2025, Vol. 46 ›› Issue (24): 332062.doi: 10.7527/S1000-6893.2025.32062

• Electronics and Electrical Engineering and Control • Previous Articles    

A robust filtering autonomous navigation method based on interactive dynamic and static multi-models in denied environments

Zihan NAN1, Ruiyang ZHOU1, Yongliang WANG1, Dayu LIU2, Ming DONG3,4(), Fanchen MENG1   

  1. 1.Beijing Institute of Aerospace Control Devices,Beijing 100039,China
    2.Beijing Institute of Electrical Engineering,Beijing 100854,China
    3.Beijing Institute of Tracking and Telecommunications Technology,Beijing 100094,China
    4.Key Laboratory of Smart Earth,Beijing 100094,China
  • Received:2025-04-02 Revised:2025-05-13 Accepted:2025-07-09 Online:2025-07-31 Published:2025-07-18
  • Contact: Ming DONG E-mail:dongmingdmdm@sina.com
  • Supported by:
    National Natural Science Foundation of China(62388101);China Aerospace Science and Technology Corporation Independent Research and Development Project(2024899);State Key Laboratory of Inertial Measurement Stabilization Support Project

Abstract:

To meet the application requirements of anti-jamming and enhanced robustness for autonomous navigation systems in complex environments, this paper proposes a Robust Cubature Kalman Filter method based on the Interacting Multiple Model (IMM-RCKF) 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 and adopting a hybrid static-dynamic filtering strategy for interactive fusion, a high-order nonlinear multi-model filter is developed. This architecture significantly enhances the environmental adaptability and interference robustness of the filter. The proposed method is implemented in the autonomous navigation system of a hypersonic aerospace vehicle. Experimental results demonstrate that the IMM-based multi-model filtering approach effectively improves the convergence speed and estimation accuracy of the navigation system. Compared with conventional robust Kalman filtering methods, the position accuracy is increased 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 dynamic uncertainties.

Key words: multi source autonomous navigation, dynamic and static filters, interacting multiple mode, GNSS denied, inertial navigation

CLC Number: