Ballistic trajectory formation method based on exponential weighting attenuated memory filtering

Abstract

The invention discloses a ballistic trajectory formation method based on exponential weighting attenuated memory filtering. During the ballistic trajectory formation process, errors generated during the trajectory model linearization process are reduced by a state deviation Kalman filter equation, and filter divergence caused by errors of the trajectory model is inhibited by the exponential weighting attenuated memory filtering. Thus, reliable filter data is provided for a trajectory prediction system. According to the invention, filter divergence can be inhibited effectively, and system stability can be raised.

Description

technical field [0001] The invention relates to a method for forming a ballistic trajectory, in particular to a method for suppressing divergence in the process of ballistic extrapolation based on exponentially weighted attenuation memory Kalman filter. Background technique [0002] After the artillery position radar obtains the trajectory of the projectile, it uses the Kalman filter data to extrapolate the ballistic trajectory to obtain the coordinates of the enemy artillery position. The Kalman filter uses the minimum mean square error criterion to estimate the dynamic parameters of the linear model where the system noise and process noise are white Gaussian noise. Since the ballistic model is a nonlinear model represented by a differential equation, in the ballistic extrapolation process, the nonlinear filtering problem is transformed into an approximate linear filtering problem by using Taylor series expansion, and then the nonlinear filtering problem is solved by using ...

AI Technical Summary

Problems solved by technology

In the filtering process, the existence of nonlinear factors has a great influence on the filtering stability and state estimation accuracy. If the ballistic model has a large error in the linearization process, the results of the extended Kalman filter will diverge; in addition, Due to the inaccurate understanding of the meteorological factors during the flight of the projectile, the ballistic model built is different from the actual physical process, and the pre-estimated process noise covariance and measurement noise covariance in the filtering process are not accurate, and error accumulation occurs during the filtering process. It also leads to filter divergence

Filter divergence prevents ballistic extrapolation from working properly

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