Statistical optimization of moving target filtering signals in artillery using Kalman

One of the most important issues in war is military defense. One of the strategically important issues in military defense is artillery support. Artillery consists of three teams, the three main artillery teams are: the forward watch team (which has the task of reporting the position of the targets), the fire control team (which performs calculations related to aiming) and finally the firing team (which executes the operation fire on the target). Because the duties of the watch team and fire control are performed by humans, the watch reports and calculations made in the fire control center have human errors, and for this reason, the armies use advanced software to improve these calculations. In this article, we are trying to obtain an algorithm to increase the accuracy of calculations in the center of artillery fire by using stochastic differential equations and Kalman filter, which are emerging and widely used mathematical tools in engineering sciences and statistics. This Kalman filter-based algorithm improves observer reports by conditional averaging of observer reports compared to previous shots (Bayesian statistics) 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 moving targets. It also has the ability to be used for moving targets with complex trajectories on the ground and in the air.