Optimization Framework of Fusion Sensor Layout for General Outpost Scientific Guard Surveillance

Abstract

This study proposes a framework for optimizing the fusion sensor layout in a future general outpost scientific guard system. The mountainous terrain near Daejeon, Republic of Korea, was designated as a surveillance area to imitate the area near the border region, and it was divided based on the importance of surveillance. A genetic algorithm was used to maximize the weighted detection coverage, where the weight was assigned based on its importance. To validate the effectiveness of the optimization, the surveillance area was divided depending on penetration probability, and 1,000 optimal penetration paths were generated using the A* algorithm. In the optimal camera layout, all paths were detected 100%. The proposed framework can be applied to the real border region and used to design the fusion sensor layout optimization in the new GOP scientific guard system.