Ballistic Trajectory Formation Method Based on Output Correlation Adaptive Kalman Filter

Abstract

The invention discloses a ballistic trajectory forming method based on output correlation adaptive Kalman filter. During the ballistic trajectory forming process, an output correlation functional matrix is estimated by the utilization of bullet flying parameters, and the optimal steady-state solution of Kalman filter gain matrix is calculated by the correlation functional matrix so as to make the steady-state gain fit in with an actual observed value; and the correlation functional matrix is continuously adjusted according to the observed value by a recursion method, and adaptive filtering estimation and correction of trajectory data are realized so as to obtain accurate ballistic trajectory. According to the invention, the filter divergence problem caused by factors such as incomplete consistency between a trajectory attribute model and an actual physical model, inaccurate system noise and observation noise parameter estimation and the like is inhibited effectively, and filtering accuracy and accuracy of the ballistic trajectory are raised.

Description

technical field [0001] The invention relates to a ballistic trajectory forming method, in particular to a ballistic trajectory forming method based on output correlation adaptive Kalman filter. Background technique [0002] After obtaining the flight parameters of the projectile, the gun position radar uses Kalman filter to filter the data to form the ballistic trajectory. The Kalman filter can obtain the optimal value under the minimum mean square error criterion under the premise of knowing the precise statistical characteristics of the random noise of the system and the precise mathematical model of the system. In the formation process of the ballistic trajectory of the gun position radar, due to the inaccurate understanding of the environment of the projectile flight, the built ballistic model is different from the actual physical process; the statistical characteristics of interference and noise are also unknown, and these factors will affect the Kalman filter algorithm...

AI Technical Summary

Problems solved by technology

In the formation process of the ballistic trajectory of the gun position radar, due to the inaccurate understanding of the environment of the projectile flight, the built ballistic model is different from the actual physical process; the statistical characteristics of interference and noise are also unknown, and these factors will affect the Kalman filter algorithm The accuracy of the Kalman filter cannot obtain the optimal estimation result, and even cause the filtering result to diverge, and the gun position radar cannot form a ballistic trajectory

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