Enhanced self-organizing map for passive sonar tracking to improve situation awareness

This paper will specifically undertake the task of improving the passive sonar system using self-organizing map. Localizing multiple targets is a challenging problem as passive sonar sensors are only able to detect the targets' bearing angle. An effective way to find the targets location is by triangulation. However, in multi-sensor multi-target environment, ghost targets are introduced during the triangulation process. Self-organizing map based on neural network is one of the most recently used methods proposed to extract the true targets. This paper will introduce two improvements to the self-organizing map. The first improvement is to initialize the neurons based on the preliminary triangulation point's distribution. This results in a faster first-time-seen of the targets. The second improvement is to apply the assumption that each bearing line is associated with only one target. This results in the reduction of the amount of false tracks detected.