The use of agent-based simulation for cooperative sensing of the battlefield

Abstract

Many military Intelligence Surveillance and Reconnaissance (ISR) operations would benefit greatly from a fleet of disparate sensor-bearing UAVs that are tightly integrated via a communications network, work cooperatively for a common operational objective, enhance situation awareness of the areas of operation, and increase persistence of sensor dwell time on strategic targets. This would enable continuity in the entire target acquisition cycle, from detection to classification to identification and finally localization of targets, in a diverse and dynamic environment. The integration of sensors and development of tactics in a cooperative sensing environment is one of the current focuses among the military intelligence community, and hence motivates this thesis effort. By building models with an existing agent-based simulation platform and using an extremely efficient experimental design methodology, numerous factors which could potentially affect the effectiveness of a cooperative sensing network against two arrays of targets are explored. The factors considered include UAV airspeed, reliability, detection/classification coverage and probability, network latency and degradation, UAV configurations and responsiveness, as well as air space separation. The two arrays of targets are mobile armor concentrations and time critical targets

these vary in their deployment profiles, vulnerability constraints and ease of detectability. Factors characterizing these targets, such as the shoot-and-scoot behavior of time critical targets, are also investigated. The study provides operational insights pertaining to the design and effective use of cooperative sensing for ISR purposes. These include the importance of having good UAV sensor capabilities, the need for a suite of sensors to aid in locating well-camouflaged time-critical targets, and the need for "intelligent" application of UAV cooperation tactics based on the characteristics of recently-classified targets.