Real-time use of Kohonen's self-organizing maps for threat stabilization

Threat evaluation is a high-level information fusion function of great importance in the command and control decision-making process. Due to highly dynamic changes in the environment, it becomes more and more difficult to assign a stable threat value to an entity. The present work aims at stabilizing the threat value assigned to a moving entity having a manoeuvring behaviour. To this end, a special type of neural networks, the self-organizing maps, is used in order to extract some important features from the kinematics of the moving entity. Its direction of advance is computed based on fusing the extracted features. The stabilization the direction of advance allows the stabilization of the threat value assigned to such an entity, especially when this value is based on the closest point of approach concept. The proposed approach is tested over a set of simulated data.