Target tracking with multipoint predictive neural network

Abstract

Frequently, multipoint target tracking is achieved using a Kalman Filter or other means. Numerous papers have been published over the past decades on tracking of dynamic systems such as ships, planes, artillery shells, and control processes with Kalman Filters, particularly, when the mathematical equations of motion describing the dynamic system are available. Then, target tracking is a fairly straight forward procedure. In this paper, a back propagation neural network is successfully `trained' for tracking an artillery shell. It is a predictive neural network because its outputs are the future positions of the artillery shell.