Today's battlefield environment contains a large number of sensors, and sensor types, onboard multiple platforms. The
set of sensor types includes SAR, EO/IR, GMTI, AMTI, HSI, MSI, and video, and for each sensor type there may be
multiple sensing modalities to select from. In an attempt to maximize sensor performance, today's sensors employ either
static tasking approaches or require an operator to manually change sensor tasking operations. In a highly dynamic
environment this leads to a situation whereby the sensors become less effective as the sensing environments deviates
from the assumed conditions.
Through a Phase I SBIR effort we developed a system architecture and a common tasking approach for solving the
sensor tasking problem for a multiple sensor mix. As part of our sensor tasking effort we developed a genetic algorithm
based task scheduling approach and demonstrated the ability to automatically task and schedule sensors in an end-to-end
closed loop simulation. Our approach allows for multiple sensors as well as system and sensor constraints. This provides
a solid foundation for our future efforts including incorporation of other sensor types.
This paper will describe our approach for scheduling using genetic algorithms to solve the sensor tasking problem in the
presence of resource constraints and required task linkage. We will conclude with a discussion of results for a sample
problem and of the path forward.