High-Speed Target Tracking and Fusion Estimation Method Based on Adaptive Improved Cubature Kalman Filter

Le résumé fourni par la source

Hypersonic target tracking is a major challenge currently faced by estimation theory, with its core difficulties lying in the strong nonlinear dynamic model, uncertain time-varying noise statistical characteristics, and complex maneuvering modes. Traditional nonlinear filtering algorithms often struggle to balance estimation accuracy, convergence speed, and robustness when addressing such problems. This paper proposes an AICKF (Adaptively Improved Cubature Kalman Filter) multi-sensor fusion estimation method based on the adaptive improvement of the traditional Cubature Kalman Filter. It constructs a unified filtering framework that organically integrates Adaptive Fading, Innovation Covariance Matching, and Regularization constraints, designs a multi-parameter collaborative optimization framework to maximize algorithm performance, and completes the process from algorithm derivation to convergence proof based on the Lyapunov method. It provides valuable parameter adjustment guidance for engineering applications through parameter sensitivity analysis. In a 500-step hypersonic target simulation scenario covering five typical flight phases, the AICKF achieves an estimation accuracy with an average position error of 4.07 meters and a root mean square error of 5.07 meters. Compared with the standard UKF and CKF, it realizes a performance improvement of 91.73% and 94.11% respectively, offering an effective solution for solving the hypersonic target tracking problem.

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