DDDAMS-Based Border Surveillance and Crowd Control via Aerostats, UAVs, and Ground Sensors

The primary goal of this research is to explore algorithmic approaches to build a robust, multi-scale, affordable, and effective border surveillance strategies for tracking aerial and ground targets (e.g. drug-smuggling UAVs) via various types of sensors in three layers. To this end, we propose a comprehensive planning and control framework based on dynamic-data-driven, adaptive multi-scale simulation (DDDAMS). Dynamic data is incorporated into the simulation process to improve its validity, at the same time simulation steers the measurement process to improve data usability. In addition, an appropriate level of simulation fidelity is selected based on the time constraints to evaluate alternative control strategies using simulation. In this research, a DDDAMS framework that was previously developed for multiple UAVs will be further extended to address effective surveillance problems and crowd control in a much boarder border area via multiple sensors in a 3-levels hierarchy including aerostats, UAVs and ground sensors. Finally, preliminary data analysis is provided for two major sensors (i.e. vision and seismic data) used in the considered surveillance application.