Target selection is the task of assigning a value or priority to various targets in a scenario. This priority is usually determined by the threat the target poses on the defender in addition to its vulnerability to possible measures to be taken by the defender. In this study, we describe a target selection technique based on neural networks. The utility or value of each target is assumed to be an unknown function acting on certain features of the target such as size, intensity, speed and direction of movement. Neural networks used in the context of function estimation is a viable candidate for determining this unknown function for generating target priorities. Various neural network configurations are examined and simulation results are presented.
Modern target tracking systems perform different tasks among which are target detection, selection and tracking. In this study, we describe a target selection technique based on Utility Theory. The utility of each target is assumed to be a linear combination of some basis functions that act on certain features of the target such as size, intensity, speed and direction. The unknown parameters of the basis functions and their weights are selected using a descent type optimization technique. Simulation results are presented.