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Learning drag coefficient of ballistic targets using gaussian process modeling

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2019
Kumru, Fırat
Ballistic object tracking involves estimating an unknown ballistic coefficient which directly affects the dynamics of the object. In most studies, the ballistic coefficient is assumed to be constant throughout the object’s flight. In reality, the ballistic coefficient is a function of the speed of the object and depends on the object’s aerodynamic properties. In the literature, the impact point prediction is defined as predicting the position that the object is expected to hit on the ground while the object is still on the fly. The accuracy of the impact point prediction highly depends on the treatment of the ballistic coefficient in the prediction model. In this thesis, we propose a method to learn the unknown function that describes the relationship between the speed and the ballistic coefficient of the object from the observations. Then, the function is used to predict the impact point of the ballistic object. The unknown function is learned via Gaussian process in the Bayesian framework. The proposed and conventional methods are comparatively studied in a realistic simulation environment. Extensive simulation studies are conducted to characterize the performance of the proposed method and it is shown that the method has a better impact point prediction performance than the conventional ones in terms of the root mean square error.