A trajectory navigation parameter estimation method based on the fourth-order Runge-Kutta numerical integration

To address the positioning accuracy limitations of trajectory correction fuzes in complex battlefield environments, this study innovatively integrates the fourth-order Runge-Kutta (RK4) numerical integration method with the Extended Kalman Filter (EKF) to propose an improved ballistic trajectory estimation algorithm. The algorithm reconstructs the Jacobian matrix computation process within the EKF framework using the RK4 method and introduces a dynamic adjustment matrix to compensate for linearization errors. A full-state estimation model for four-degree-of-freedom ballistic motion is re-derived and established. Simulation results demonstrate that the new method achieves significant improvements over the traditional EKF algorithm. Compared to other optimization methods, it provides comparable positional estimation accuracy while achieving superior velocity estimation precision, also maintaining robust performance under extreme conditions. Furthermore, field firing tests validate the algorithm's effectiveness and stability in real-flight environments, laying a foundation for subsequent high-efficiency correction control.