Statistical modeling and optimization of targeting fixed targets in artillery with Kalman filter

In military defense, artillery fire support is strategically important. Artillery consists of three teams: the forward observation team (which has the task of reporting the position of the targets), the fire control team (which performs the calculations related to aiming) and finally the firing team (which executes the operation of fire on the target). he is doing it). The watch reports and calculations made in the fire control center always have human errors, and for this reason, the armies use advanced software to improve these calculations. In this article, using stochastic differential equations and Kalman filter, which are emerging and widely used tools in engineering sciences and statistics, we obtained an algorithm to increase the accuracy of calculations in the artillery fire control center. This Kalman filter-based algorithm improves observer reports by conditional averaging of observer reports compared to previous shots and considering observer and artillery error, and is used in the artillery fire control center. Also, the obtained algorithm is presented in a parametric form, that is, it can be adjusted in different battlefield conditions based on the artillery officers' knowledge of the weather conditions and the observation error and the weapons used. This algorithm is obtained in closed form for fixed objectives.