Sensor Platform Optimization and Simulation for Surveillance of Large Scale Terrains

Surveillance of large terrains using limited sensor capabilities is a challenging task in many military applications. In this paper, we present a new method to determine the number, type and location of sensor platform systems to effectively cover a large terrain, which is composed of areas of varying importance. A sensor platform system is an integrated system that consists of a platform (human, jeep, aircraft, etc.) and one or more sensors (day-tv, infra-red, radar, etc.) integrated to that platform. The objective in the optimization is twofold: first, determine locations of a given set of sensor platform systems in order to effectively cover areas in accordance with importance. Second, determine the number, type and locations of sensor platform systems to effectively cover the terrain. We developed a genetic algorithm to solve this optimization problem. In the optimization, user may specify a budget and the tool may be run to determine the number, type and locations of the sensor platform systems within this budget to maximize effective coverage as much as possible. Alternatively the tool may be run to guarantee a predefined effectiveness measure concerning coverage, minimizing total cost of sensor platform systems. In order to simulate optical sensors and radars, we developed a generic probabilistic sensor model, which is based on line-of sight and ray tracing. This stochastic modeling allows us to simulate detection, recognition and identification capabilities of different types of sensors on high-resolution terrains. The next part of that study is a distributed human in the loop simulation, which is developed to demonstrate the usage of the sensor platforms under the control of a tactical level control center. To perform the simulation, a tactical command center, an HLA compliant physical radar simulator, a number of sensor platform consoles and semi-automated agents are developed.