Proceedings Article | 18 May 2012
J. DeSena, S. Martin, J. Clarke, D. Dutrow, A. Newman
KEYWORDS: Sensors, Intelligence systems, Surveillance, Kinematics, Data fusion, Data modeling, Telecommunications, Process control, Control systems, Image sensors
As the number and diversity of sensing assets available for intelligence, surveillance and reconnaissance (ISR)
operations continues to expand, the limited ability of human operators to effectively manage, control and exploit the ISR
ensemble is exceeded, leading to reduced operational effectiveness. Automated support both in the processing of
voluminous sensor data and sensor asset control can relieve the burden of human operators to support operation of larger
ISR ensembles. In dynamic environments it is essential to react quickly to current information to avoid stale, sub-optimal
plans. Our approach is to apply the principles of feedback control to ISR operations, "closing the loop" from the sensor
collections through automated processing to ISR asset control.
Previous work by the authors demonstrated non-myopic multiple platform trajectory control using a receding horizon
controller in a closed feedback loop with a multiple hypothesis tracker applied to multi-target search and track
simulation scenarios in the ground and space domains. This paper presents extensions in both size and scope of the
previous work, demonstrating closed-loop control, involving both platform routing and sensor pointing, of a multisensor,
multi-platform ISR ensemble tasked with providing situational awareness and performing search, track and
classification of multiple moving ground targets in irregular warfare scenarios. The closed-loop ISR system is fullyrealized
using distributed, asynchronous components that communicate over a network. The closed-loop ISR system has
been exercised via a networked simulation test bed against a scenario in the Afghanistan theater implemented using
high-fidelity terrain and imagery data. In addition, the system has been applied to space surveillance scenarios requiring
tracking of space objects where current deliberative, manually intensive processes for managing sensor assets are
insufficiently responsive. Simulation experiment results are presented.
The algorithm to jointly optimize sensor schedules against search, track, and classify is based on recent work by
Papageorgiou and Raykin on risk-based sensor management. It uses a risk-based objective function and attempts to
minimize and balance the risks of misclassifying and losing track on an object. It supports the requirement to generate
tasking for metric and feature data concurrently and synergistically, and account for both tracking accuracy and object
characterization, jointly, in computing reward and cost for optimizing tasking decisions.
